This session aims to bring together the latest research quantifying and understanding high-impact climate events in past, present and future climates. We welcome studies across all spatial and temporal scales, and covering compound, cascading, and connected extremes as well as worst-case scenarios, with the ultimate goal to provide actionable climate information to increase societal preparedness to such extreme high-impact events.
We invite work addressing high-impact extreme events via, but not limited to, model experiments and intercomparisons, diverse storyline approaches such as event-based or dynamical storylines, climate projections including large ensembles and unseen events, insights from paleo archives, and attribution studies. We also especially welcome contributions focusing on physical understanding of high-impact events, on their ecological and socioeconomic impacts, as well as on approaches to potentially limit societal impacts.
The session is sponsored and closely linked to the World Climate Research Programme lighthouse activitIES on 'Understanding High-Risk Events' and 'Explaining and Predicting Earth System Change'.
Orals: Tue, 5 May, 08:30–15:45 | Room F1
Short-duration, high-intensity rainfall extremes associated with convective storms pose a growing risk to urban areas under a warming climate, yet their future evolution remains difficult to quantify at the global scale using existing modelling approaches. Local projections are often constrained by the lack of long high-resolution observations and by the limited ability of climate models to accurately simulate sub-daily precipitation processes at the global scale. Here, we present a globally applicable framework for projecting changes in rare, short-duration rainfall extremes using temperature as a covariate in a non-stationary extreme value framework building on the TENAX model, driven entirely by global climate model output and without reliance on local observational data. The focus on rare, short-duration extremes directly targets the class of events responsible for a disproportionate share of climate-related impacts.
The approach links changes in rainfall intensity distributions to projected shifts in wet-day temperature distributions from CMIP6 models, integrating over the full temperature distribution rather than relying on uniform scaling or mean-shift assumptions. Dew-point temperature is employed as a proxy for atmospheric moisture availability, allowing thermodynamically constrained intensification of convective rainfall extremes to be represented consistently across climates. In an initial multi-regional application, the framework projects robust intensification of hourly-scale rare rainfall events, with increases of order 10–20% by late century under intermediate emissions scenarios and substantially larger changes under high-emissions pathways. Accounting for changes in the full temperature distribution shows that the strongest intensification occurs for the rarest events, which is underestimated when intensities are scaled only by mean temperature changes.
We further extend the framework to a global scale to assess spatial patterns and key structural uncertainties in projected short-duration rainfall intensification. Results highlight that methodological choices, including the selection of temperature covariate (dew-point versus surface air temperature), can introduce differences comparable to inter-model climate uncertainty in some regions, particularly in moisture-limited and continental climates. Treating these choices explicitly as structural uncertainties provides a clearer interpretation of projection robustness across diverse hydroclimatic regimes and highlights uncertainties beyond inter-model spread alone.
Overall, this work demonstrates that temperature-covariate approaches, when carefully formulated and driven by global climate models, offer a transferable and physically grounded pathway for projecting rare, short-duration rainfall extremes worldwide. The framework enables consistent global assessments in data-scarce regions and supports climate-change impact studies and urban adaptation planning by explicitly quantifying the uncertainties that matter most for short-duration rainfall risk.
How to cite: Blagojević, J., Prein, A., Peleg, N., and Molnar, P.: Global projections of short-duration rainfall extremes using temperature-covariate models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13482, https://doi.org/10.5194/egusphere-egu26-13482, 2026.
Short-duration extreme precipitation is a key driver of urban flooding and associated socio-economic impacts in a warming climate. Increasing urbanization further amplifies the vulnerability of cities to intense rainfall occurring over minutes to hours. These extremes frequently trigger flash floods and pose substantial risks to urban infrastructure and public safety. Despite growing recognition of its importance, regional-scale assessments of sub-hourly extreme precipitation remain limited. Emerging observational evidence indicates that short-duration precipitation events (≤1 hour) are intensifying at a faster rate than longer-duration events. In this study, we analyze short-duration extreme precipitation events at 5-, 10-, 20-, 30-, and 60-minute timescales using observations from 16 automated weather stations (AWS) across the rapidly urbanizing Greater Sydney region, New South Wales, Australia. Our results show a pronounced increasing trend in extreme precipitation at higher percentiles, particularly at the 5–10 minute timescales, compared to hourly extremes. At the hourly scale, we evaluate the performance of five convection-permitting regional climate model simulations (4 km ensemble) against AWS observations. The models reasonably capture the upper tail of the precipitation distribution but tend to slightly overestimate the frequency of extreme events. To assess future changes, we examine the intensity of 99th percentile precipitation extremes across three periods—historical (1951–2014), near future (2015–2057), and far future (2058–2100)—under three Shared Socioeconomic Pathway scenarios (SSP126, SSP245, and SSP370). The projections indicate a consistent intensification of extreme precipitation, with a substantial upward shift in the top 1% of historical extremes, most pronounced under the high-emission SSP370 scenario. Interestingly, the simulations also project a reduction in the total number of wet hours relative to the historical baseline, suggesting a transition toward shorter-duration but more intense precipitation events. Although considerable inter-model spread and spatial variability exist, increases in 99th percentile extremes are robust across most scenarios. Notably, under SSP126, a decline in extreme precipitation is projected in the far future, highlighting the potential benefits of strong emission mitigation. These findings underscore the need to explicitly incorporate short-duration precipitation extremes into urban planning and flood risk management under climate change.
Keywords: Automatic Weather Station, Climate change, Flash floods, NARCliM2.0, Regional climate models, Sub-hourly extreme precipitation
How to cite: Khadke, L., Evans, J. P., Kim, Y., Virgilio, G. D., and Kala, J.: Intensification of Short-Duration Extreme Precipitation in Greater Sydney, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15607, https://doi.org/10.5194/egusphere-egu26-15607, 2026.
In the aftermath of extreme weather, policy makers, contingency planners and insurers often seek to understand the likelihood of experiencing such events. The most common tool for this is extreme value analysis (EVA), but likelihood estimates based on observed or reanalysis data can be highly uncertain due to the relatively short observational record. Substantially larger samples of plausible extreme weather events can be obtained using the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach, which involves applying EVA to large forecast/hindcast ensembles. While larger sample sizes generally reduce the uncertainty associated with EVA, using seasonal or decadal forecast data introduces additional uncertainties related to model bias and model diversity. In this study, a multi-model ensemble of hindcast data from the CMIP6 Decadal Climate Prediction Project was analysed to quantify these additional uncertainties in the context of extreme temperature and rainfall across Australia. Factoring in model bias and diversity dramatically increased the uncertainty associated with estimated event likelihoods from the UNSEEN approach, to the point that it equaled or exceeded the uncertainty from an observation-based approach at most locations. Model diversity tended to be the largest source of uncertainty (60-70% of the total). Bias correction was also a significant source of uncertainty (30-40%), while the uncertainty associated with EVA was trivial. Our results suggest that an UNSEEN-based approach to estimating the likelihood of climate extremes should be understood as an approach that has different uncertainty characteristics to an observation-based approach, as opposed to less uncertainty.
How to cite: Irving, D., Stellema, A., and Risbey, J.: Quantifying the uncertainty associated with extreme weather likelihood estimates derived from large model ensembles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14625, https://doi.org/10.5194/egusphere-egu26-14625, 2026.
How the frequency and intensity of extreme weather events is affected by global warming in different regions is one of the central questions of climate change research, with obvious direct implications for climate change adaptation. A standard approach of defining weather extremes is to consider the exceedance of a percentile threshold, calculated from the statistical distribution of a meteorological variable of interest in a predefined reference period. Trends can then be assessed by considering the frequency of threshold exceedances in a period that extends beyond the reference period. While this approach appears rather straightforward, it comes with several choices related to the parameter, percentile threshold, aggregation period, reference period, and boosting interval. Here aggregation period refers to the question whether, e.g., precipitation extremes are considered with a duration of 1 hour or 1 day or multiple days, and the boosting interval is the symmetric time window used to calculate percentiles for a given day of year. When checking these partly methodological choices in previous studies, e.g., those referenced in the IPCC report, it becomes evident that different studies made different choices. Since there is no obvious “best choice”, it is important to quantify the influence of these choices on the resulting trend estimates. Therefore, this study uses ERA5 reanalysis data to systematically and globally explore the trends in 2-m temperature (T2m) and precipitation (P) and their robustness with respect to the aforementioned parameters. Key results are that (i) trends vary strongly between regions, (ii) they are methodologically more robust for T2m than for P, (iii) in regions with weak P trends, the sign of the trend depends on the methodological choices. These explorative analyses with ERA5 data are complemented by synthetic data experiments, in particular to investigate the influence of the boosting window. We suggest that trend analyses of percentile threshold exceedances of any parameter in any dataset should consider these methodological sensitivities in order to communicate robust estimates.
How to cite: Wernli, H., Sternal, T., Voigt, S., Sprenger, M., and Hoefler, T.: How reliably can we estimate trends of surface weather extremes? A conceptual study using ERA5 reanalyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12895, https://doi.org/10.5194/egusphere-egu26-12895, 2026.
Obtaining robust and actionable information on regional precipitation change to enable adaptation planning and decision-making is a matter of great concern, since there are multiple sources of information. Projections from large CMIP6 model ensembles (e.g., IPCC Interactive Atlas) show weak signal of climate change in total annual and seasonal precipitation over most of South America (SA), with low agreement between models. Besides, information from smaller ensembles is frequently discrepant. A dynamic framework for climate change in SA is necessary to achieve robust and actionable changes.
Even though they are weak and not robust, the precipitation changes produced over SA by large model ensembles suggest that their main driver is the ENSO increased variability in eastern Pacific, especially intensified El Niño events, produced by transient greenhouse-gas-induced warming. This is consistent with the large impact of ENSO on precipitation in SA. This dynamical framework requires that models used for climate projections in SA demonstrate good simulation not only of the climatology, but also of ENSO and its teleconnections with SA. The assessment of 31 models that provided at least three runs from the present (1979-2014) to the future climate (2065-2100), based on both criteria, selected five best-performing models. This reduced set accurately reproduces the observed seasonal impact of ENSO on precipitation in SA and produces strong and robust patterns of climate change with seasonal variation dynamically consistent with more intense future ENSO in a more El Niño-like mean state.
Since the most dramatic impacts of climate change are produced by changes in the frequency and intensity of extreme precipitation events, it is essential that robust and actionable information is also provided on changes of these events, defined as above the 90th percentile. The analysis is based on the same dynamic framework of the changes in total seasonal/monthly rainfall, since ENSO also exerts a large impact on the extreme events in SA, and the selected set of models shows good simulation of the observed seasonal/monthly impact of ENSO on the frequency and intensity of extreme events. The available information usually shows changes of annual extreme indices. We adopt a seasonal/monthly resolution, which is very useful, especially in a monsoon regime with pronounced annual precipitation cycle. The future changes in extreme events is obtained for SA with monthly temporal resolution and 1 degree spatial resolution. The patterns of change in frequency and intensity of extreme events do not coincide, as changes in frequency depend on dynamic changes, while changes in intensity also depend on thermodynamic changes that determine the precipitable water vapor. Patterns of change in the frequency of extreme events in future are similar to the patterns of El Niño impact on the frequency of extreme events in the present. Changes in the average intensity of precipitation in future extreme events are generally positive and predominate in southeastern South America, where the frequency also generally increases, maximizing impacts on densely populated areas of great importance for agricultural and energy production. The provided information contributes to increase societal preparedness to extreme precipitation in SA.
How to cite: Grimm, A. M., Fanderuff, L. G., and Saboia, J. P. J.: Robust and actionable information on climate change and extreme rainfall events in South America, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20226, https://doi.org/10.5194/egusphere-egu26-20226, 2026.
Many of the most societally impactful weather events in Europe occur on short timescales and there is a growing demand for improved projections of how such extremes will change in the future. That is, how will global climate change over decades impact extreme weather over days? The multiscale nature of this question challenges the capabilities of current earth system models, and this is especially the case for hydrometeorological extremes. Accurately simulating the hazards posed by extreme precipitation requires faithfully resolving interactions between the large-scale circulation, synoptic dynamics, the local boundary-layer, and hydrological and land surface conditions.
This is not only a quantitative modelling challenge, but a challenge of interpretation and narrative: the dynamics of extreme precipitation are diverse across space and time, and the statistics of the highest impact events are necessarily poorly constrained. These challenges are complicated further by the evergrowing size and hetereogeneity of multi-model datasets How can we explain model biases and trends in extreme precipitation? When models project similar changes in hydrometeorological risk do they do so for the same reasons? What implications do these factors have for regional downscaling and impact modelling? Can we relate future extremes quantitatively and robustly to historical high-impact events, as often requested by societal stakeholders?
We tackle these questions through a novel flow-precursor framework, applied to observational data, large ensemble climate simulations and subseasonal weather forecasts. We decompose extreme event risk into contributions from different scales and flow conditions, using regionally specific synoptic flow precursors which are directly associated with individual high-impact extremes or classes of extreme. These precursors are algorithmically identified and can be easily computed in large datasets, allowing us to obtain a physical interpretation of changing extreme risk across Europe without obscuring regional or seasonal diversity in precipitation dynamics.
We show how climate model biases and forced changes in extreme precipitation can be explained, categorised, and visualised in a succinct way that highlights important differences in their suitability for use in downscaling, impact modelling and storyline development. We demonstrate how dynamical decomposition can extract usable climate information even from heavily biased models, and how insights from models at different scales–such as from large climate ensembles and high-resolution weather forecasts–can be quantitatively synthesised to provide new insights on future hazards and plausible worst-case scenarios. Finally, we show how the method can be used to reframe complex, probabilistic climate projections and weather forecasts in terms of individual high impact historical events, aiding scenario visualisation, and allowing stakeholders to leverage their experience and domain knowledge when preparing for future high-impact extremes.
How to cite: Oldham-Dorrington, J., Li, C., Sobolowski, S., Guillaume-Castel, R., and Lutzmann, J.: Understanding, interpreting, and communicating future extreme precipitation risk using flow precursors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13670, https://doi.org/10.5194/egusphere-egu26-13670, 2026.
While the performance of climate models in simulating the magnitude of global warming has been extensively assessed, their fidelity in representing the three-dimensional (3-D) structure of warming, and how this affects extreme event attribution, remains poorly understood. Pseudo-global-warming experiments implicitly assume that imposed anthropogenic warming perturbations realistically capture the observed vertical and horizontal distribution of atmospheric temperature change. However, this assumption is rarely evaluated explicitly.
We diagnose 3-D structural warming discrepancies by comparing a representative set of six CMIP6 climate models against ERA5 temperature trends over 1940–2024. We show that widely used models exhibit systematic vertical and horizontal warming biases, typically over-amplifying warming in the mid-to-upper troposphere while damping the response near the surface, particularly across Northern Hemisphere mid-latitudes. We further show that these structural biases propagate into substantially different estimates of extreme rainfall intensification.
Using an ensemble of 81 high-resolution MPAS simulations within a storyline attribution framework, we analyze the October 2024 Valencia flood-producing storm as a high-impact case study. The diagnosed anthropogenic rainfall signal is highly sensitive to the 3-D structure of the imposed warming: CMIP6-based counterfactual experiments yield weak reductions in extreme rainfall (~10%), whereas observation-constrained warming profiles produce a stronger and more significant anthropogenic contribution (~30%). This amplification arises from enhanced low-level moistening and increased convective instability, together with dynamically consistent upper-level flow strengthening. The results confirm that 3-D warming structure is a first-order control on extreme-rainfall attribution, and that persistent model-structural errors can lead to a systematic underestimation of attribution signals in mid-latitude, high-impact precipitation extremes.
How to cite: Insua Costa, D., Lemus Cánovas, M., Senande Rivera, M., M. H. Deman, V., L. Geirinhas, J., and G. Miralles, D.: Extreme rainfall attribution distorted by structural warming biases in climate models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10410, https://doi.org/10.5194/egusphere-egu26-10410, 2026.
On 1 October 2020, the intense extra-tropical storm Alex impacted the north-west coast of France, producing unusually strong wind gusts for the season. On 2 October, the storm triggered record-breaking rainfall over the south-eastern French Alps and north-western Italian Alps. In France, this Heavy Precipitation Event (HPE) caused severe flooding and landslides, resulting in casualties, and over 1 billion euros in economic losses.
We used convection-permitting regional climate modeling with a spatial resolution of 2.5 km to investigate these observed events. Simulations were conducted over September-October 2020 on an extensive domain centered on France. Our model successfully reproduces the characteristics of both the HPE and storm Alex, including the observed sequence of events and impacts (Bador et al., 2025).
We then explored how the observed 2020 Mediterranean HPE could have been different had it occurred 2 years later, in 2022, where warmer sea surface was recorded in the western Mediterranean Sea. This storyline analysis suggested reduced precipitation impacts over the south-eastern French Alps but enhanced impacts in Italy. Additional sensitivity experiments confirmed the key role of regional sea surface temperatures (SSTs) in shaping the HPE’s intensity in the western Alps, with an eastward shift of heavy precipitation with higher Mediterranean SSTs. Our simulations consistently show that sea surface warming can further intensify the Mediterranean HPE, while cooling reduces the intensity of extreme precipitation and local impacts. In contrast, modifications to the Atlantic SSTs affecting storm Alex itself have a limited influence on the regional Mediterranean circulation and the HPE.
All simulations were performed using initial-condition large ensembles to assess the role of internal variability in shaping local extremes. We highlighted variations among ensemble members in both local rainfall extremes and in gustiness. As impact sectors increasingly rely on km-scale climate modelling to inform local climate change assessments, our results underscore the importance of the ensemble-based approaches to fully capture the range of possible outcomes for extreme events locally.
How to cite: Bador, M., Noirot, L., Caillaud, C., and Boé, J.: Cooler than observed sea surface could have reduced impacts of storm Alex and induced mediterranean heavy precipitation event in France, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16649, https://doi.org/10.5194/egusphere-egu26-16649, 2026.
Global surface air temperature (GSAT) reached unprecedented heights in 2023. The record of year-to-year temperature increases was surpassed by a significant margin, especially in early boreal fall. We attribute the majority of this seasonal jump to the onset and maturing stages of the 2023 El Niño event. Using a process-based analysis of multiple observational datasets, we show that the uniqueness of the 2023 event can be largely related to the La Niña-like ocean-atmosphere background state upon which it developed.
This resulted in (1) a steep year-to-year increase of Sea Surface Temperature (SST), particularly in mean atmospheric subsidence regions, leading to extreme reduction of low-cloud-cover and giving rise to a record-breaking change in the radiative budget over the central and eastern Indo-Pacific; (2) anomalous sustained precipitation over climatological high SSTs in the Western Pacific, fueling unusual diabatic heating and an exceptionally early increase in tropical tropospheric temperature in boreal fall, ultimately influencing the GSAT jump with an additional contribution from the North Atlantic.
Our study improves the understanding of the interactions between interannual internally-driven processes and changes in mean climate background state, which a changing background is crucial to assess the evolution and modulation of anthropogenically-driven trends.
How to cite: Mex, J., Cassou, C., Jézéquel, A., Bony, S., and Deser, C.: Why was the 2023 jump in global temperature so extreme?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4263, https://doi.org/10.5194/egusphere-egu26-4263, 2026.
Cold extremes — also referred to as cold air outbreaks, cold spells, or cold snaps — have received less attention in the scientific literature than hot extremes, largely because their frequency and intensity are projected to decrease under climate change. Nevertheless, cold extremes continue to exert substantial impacts across a wide range of sectors, including human health, agriculture, and infrastructure. Superimposed on their overall global decline is pronounced regional and seasonal variability, driven by variability in the underlying physical mechanisms, which themselves may be influenced by climate change. Here, we provide an overview of global and regional trends in cold extremes, examine their key drivers in both present and future climates, and discuss outstanding questions related to the dynamical forcing of cold extremes and their projected evolution under climate change.
How to cite: Domeisen, D., Afargan-Gerstman, H., Blackport, R., Butler, A. H., Hanna, E., Karpechko, A. Yu., Kretschmer, M., Lee, R. W., Maycock, A., Russo, E., Shen, X., and Simpson, I. R.: Trends and Drivers of Cold Extremes in a Changing Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16825, https://doi.org/10.5194/egusphere-egu26-16825, 2026.
Virtually all land regions have warmed over recent decades, yet heatwave trends show striking regional differences. The thermodynamic rise of hot extremes can be strongly modulated by atmospheric circulation, a phenomenon that has received increasing attention for regions such as Europe and parts of North America, where observed trends in hot extremes have been amplified and dampened, respectively. But what about other regions? How persistent are these circulation anomalies? And what are the implications for future heatwaves?
Using dedicated climate model experiments, we quantify how atmospheric internal variability has modulated historical heatwave trends globally. Building on a large ensemble framework, we interpret observed circulation contributions as placing regions on unusual warming trajectories — either well below or above the ensemble mean expectation. Regions currently displaying less warming compared to climate model simulations are effectively "lagging behind" the warming already committed to by anthropogenic forcing; those running warm are "ahead".
This warming trajectory position has profound implications for the pace of future change. Regions currently lagging behind, including much of North America, face substantially faster increases in hot extreme probability between now and the mid-century than ensemble mean projections suggest. Conversely, other regions have already experienced much of the expected probability increase. We illustrate these divergent futures through the evolving return period of what was once a 1-in-100-year hot extreme, showing how the present trajectory position determines the pace of change over the coming decades.
How to cite: Schumacher, D. L., Bauer, V., Gu, L., Pierini, L., and Seneviratne, S. I.: Behind or ahead of committed warming: what it means for future hot extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13840, https://doi.org/10.5194/egusphere-egu26-13840, 2026.
What are the worst-case heatwaves that are plausible in the present or near-future climate? Model-based experiments using ensemble boosting, a computationally efficient method to simulate unprecedented extremes, suggest that month-long heatwaves that break previous records by more than 5 K across Germany and France are possible in the near future. But how can we assess the plausibility of these heatwaves unprecedented in the observational record? We here test whether the most extreme simulated month-long heatwaves in Germany and France are consistent with current process understanding and with historical heatwaves.
We show that despite their extreme record-breaking characteristics both events cannot be ruled out as implausible. To demonstrate this, we compare these two worst-case events with historical heatwaves in the reanalysis record. To this end, we calculate standardized anomalies relative to a time-evolving climatology of relevant physical variables such as temperature, 500 hPa geopotential, surface solar radiation, and soil moisture. We focus on two different worst-case events — one in Germany and one in France — which exhibit distinct characteristics and physical drivers. The event in Germany features extreme anomalies in most physical drivers, particularly those associated with land-atmosphere feedbacks, and features three short heatwaves in quick succession. In contrast, the event in France mostly features less extreme anomalies in these drivers and consists of one less intense but very persistent heatwave caused by anomalously weak zonal flow combined with above-average southerly winds. Using a multilinear statistical model and comparing with historical analogues, we show that the characteristics and physical drivers of both events are consistent with current process understanding and with historical events.
How to cite: Roemer, F. E., Fischer, E. M., Noyelle, R., and Knutti, R.: Demonstrating the plausibility of worst-case month-long heatwave storylines in Western Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3456, https://doi.org/10.5194/egusphere-egu26-3456, 2026.
The formation of surface heat extremes is usually described in terms of surface processes and upper-level dynamics. However, their full vertical temperature profile contains additional essential information about the involved processes and dynamics. So far, it remains unclear whether heat extremes are associated with characteristic vertical temperature anomaly profiles and how they vary across the globe.
In this study, we globally and systematically classify vertical temperature anomaly profiles during annual maximum 2-m temperatures, so-called TXx events, using a k-means clustering approach. After a suitable normalisation and scaling of the anomaly profiles, we find three clusters, whose global distribution closely follows the polar, mid-latitude, and tropical climate zones. The three clusters capture key structural differences of heat extremes. Within the tropical cluster, positive temperature anomalies during TXx events are confined to the (often deep) boundary layer and intensify progressively in the days leading up to the event, while the upper troposphere is not deviating from its climatological mean. The mid-latitude cluster also exhibits bottom-heavy temperature anomalies, which, however, extend throughout the full troposphere, showing a strong vertical coupling during heat extremes. In the polar cluster, heat extremes are characterised by deep tropospheric warm anomalies, accompanied by the erosion of the near-surface inversion layer, resulting in a shallow layer of particularly strong temperature anomalies near the ground.
These results show that while multiple physical mechanisms can generate a heat extreme, at first order, temperature anomaly profiles during heat extremes are very similar to each other within a given climate zone. The variability between TXx events is much larger than the variability between the median profile of different grid points in the same cluster. Besides, the temperature profiles of the most extreme events are more similar to those of their cluster than the more moderate events, suggesting a typical dynamics of the most extreme heat events.
How to cite: Hotz, B., Wernli, H., and Noyelle, R.: Global characterisation of the vertical temperature anomaly structure of heat extremes over land in ERA5, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13076, https://doi.org/10.5194/egusphere-egu26-13076, 2026.
How hot can heatwaves get in a given region of the world? The current pace of climate change challenges the statistical methods traditionally used to answer this question. An alternative approach is to seek a physics-based upper bound to extreme surface temperatures (Ts). Recent work proposed to address this problem using the hypothesis that convective instability limits the development of heat extremes. Here, we show that under this hypothesis, the absolute upper bound for extreme Ts --- obtained in the limit of zero surface humidity --- is set by dry convection: that is, this bound is reached when the mid-troposphere and the surface are connected by a dry adiabat. Previous work suggested that this upper bound is instead set by moist convective instability and is several degrees hotter. We resolve this discrepancy by showing that moist convection only limits heatwave development when surface specific humidity is larger than a threshold, and that the moist convective upper bound cannot exceed the dry limit. Yet, numerous temperature profiles in observational and reanalysis records do exceed the dry convective limit. We show that these occur exclusively in regions with an extremely deep boundary layer and where a daytime superadiabatic layer develops near the surface. We conclude with an overview of the different upper bounds applicable in dry and moist scenarios, including the roles of processes such as entrainment and convective inhibition.
How to cite: Nicolas, Q. and Hotz, B.: Dry and moist convective upper bounds for extreme surface temperatures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2537, https://doi.org/10.5194/egusphere-egu26-2537, 2026.
The increasing frequency of Mediterranean heatwaves is associated with widespread impacts on human health, agricultural productivity, and infrastructure. Previous studies have shown that large-scale circulation patterns, such as persistent ridges and atmospheric blocking, play a key role in triggering heatwaves, along with subsidence and warm-air advection. However, the intensity and persistence of these events depends not only on the advection of heat and moisture but also on the heat and moisture supplied by turbulent surface fluxes into the advected air mass. Sensible and latent heat fluxes modify air-mass temperature and humidity (and thus equivalent potential temperature) along transport pathways to the heatwave region. These flux contributions, and their relative importance for heatwave anomalies, remain uncertain.
In this study, the contribution of surface sensible and latent heat fluxes to near-surface moisture and temperature anomalies during heatwaves is quantified using a new Lagrangian framework that combines backward air-mass trajectories from the FLEXPART particle dispersion model with surface fluxes from ERA5 reanalysis data. Surface flux contributions to the moist static energy are estimated by coupling them with near-surface residence times of air parcels arriving in the heatwave region. The approach is first validated by showing that moist static energy at the heatwave location can be reproduced by the sum of the particle initial conditions (i.e., most static energy at trajectory termination points) and the surface flux contributions accumulated over the Lagrangian tracking period. Following this validation, surface flux contributions can be split into latent and sensible heat flux contributions and mapped geographically.
The method is then applied to two recent Mediterranean heatwaves to assess the relative roles of sensible and latent heat fluxes and to identify the dominant land and sea source regions. Overall, this framework provides a direct and physically consistent way to attribute the moist static energy associated with heatwaves to surface fluxes, offering new insights into the processes that build and maintain Mediterranean heatwaves.
How to cite: Deshmukh, V., Stohl, A., and Dütsch, M.: Surface flux contributions to Mediterranean heatwaves: a new Lagrangian diagnostic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12438, https://doi.org/10.5194/egusphere-egu26-12438, 2026.
Quantifying the influence of anthropogenic global warming on extreme events requires both physical and statistical understanding. We present a framework combining two complementary conditional attribution methods: spectrally nudged storylines and flow-analogues. The storyline approach provides insights on how a specific event is shaped by the thermodynamic conditions representing past (counterfactual), present (factual) and future global warming levels (+2K, +3K, +4K). The flow-analogue method provides a statistical analysis of the recurrence of the observed event, and the future storyline-projected events based on similar dynamical patterns that lead to the event of interest. Together, this combined approach allows us to determine not only the change in likelihood of an extreme event occurring as it did in the present, but also the probability that an intensified version (storyline-projected) of it occurred in the future.
Applied to the 2018 Central European heatwave, storylines show an area-mean warming rate of 1.7 °C per degree of global warming. Through the flow-analogue method, it was evidenced that the atmospheric blocking leading to this event remains equally likely to occur regardless of global warming. Despite it, the storyline-projected intensities might become more frequent and extreme at their corresponding warming levels than the factual 2018 event was under present conditions. Specifically, the 2018 heatwave, with an intensity of 2.2 °C and a return period of 1-in-277-years today, is projected to intensify to 6.6 °C with a 1-in-26-years return period in a +4K world. This behavior revealed the importance of other physical mechanisms and interactions influencing the occurrence and intensification of heatwaves beyond the atmospheric circulation pattern and thermodynamic conditions. We conclude that this combined framework is promising for climate change attribution of individual extreme events, offering both a physical assessment of anthropogenic warming and its associated likelihood while accounting for potential shifts in atmospheric dynamics.
How to cite: León-FonFay, D., Lemburg, A., Fink, A. H., Pinto, J. G., and Feser, F.: A combined storyline-statistical approach for conditional attribution of climate extremes to global warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14325, https://doi.org/10.5194/egusphere-egu26-14325, 2026.
Record-breaking heat extremes imply large health risks and can disrupt critical infrastructure, because societies are often adapted only up to previously observed extremes. Understanding how new records evolve is therefore essential. The probability of record-breaking heat events depends on the regional warming rate. This rate is mainly driven by greenhouse gas-induced global warming and has increased in recent decades. The resulting annual probability of record-breaking heat extremes is additionally modified in a nonlinear way by other regional forcing changes, such as aerosols. Because aerosol concentrations have changed substantially in many regions, they can amplify or reduce the annual likelihood of exceeding previous temperature records.
We first analyze single forcing large ensemble simulations that isolate the effects of aerosols and greenhouse gases. In Europe, decreasing aerosol concentrations have increased the regional warming rate and thereby the probability of record-breaking heat extremes by about 35% today. In contrast, in South Asia, where aerosol concentrations are increasing, we find a dampening of record-breaking probabilities of about 40%. To evaluate the effect of near-future aerosol reductions, we use simulations from the Regional Aerosol Model Intercomparison Project (RAMIP). In RAMIP, aerosol emissions are reduced from SSP3-7.0 to SSP1-2.6 either globally or only in selected regions. This allows us to analyze the regional effects of aerosol reductions as well as their remote responses. In general, aerosol reductions lead to an increased probability of record-breaking heat extremes.
Finally, we examine recent observed record-breaking events and evaluate whether their regional frequency matches the expected record breaking probabilities from model simulations. We expect that changes in aerosol concentrations contribute to changes in the annual record-breaking probability in regions with major aerosol concentration changes in recent decades, such as Europe, North America, East Asia, and South Asia. Overall, these results suggest that changes in aerosol concentrations are important for the present and near-future probability of record-breaking heat extremes.
How to cite: Kraulich, F., Pfleiderer, P., and Sippel, S.: Regional aerosol changes modulate the odds of record-breaking heat extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1535, https://doi.org/10.5194/egusphere-egu26-1535, 2026.
Reaching net zero greenhouse gas emissions is essential to halt the current global warming trend and attempt to stabilise global temperatures. However, uncertainties remain on the sign and the magnitude of the long-term responses of the climate system following anthropogenic emissions cessation.
This study contributes to constraining this uncertainty by exploring the global and regional temperature evolution under zero CO2 emissions conditions in the UKESM1.2 projections following the TIPMIP protocol (Jones et al., 2025). Stabilised warming levels spanning +1.5°C to +5°C above pre-industrial conditions are analysed to understand the impact of antecedent conditions on post zero-emissions trends. We find that the global average surface air temperature (GSAT) keeps increasing in all stabilised warming scenarios. The increase is more pronounced in the +3°C to +5°C scenarios, where it approaches 0.25°C per century. Most of the warming is registered in the Southern Hemisphere, particularly in the Southern Ocean, while the Northern Hemisphere experiences a slight cooling trend over land.
These regional cooling trends are more marked for the annual temperature maxima, with several regions across 45-65°N experiencing cooling of >1°C per century. The strongest cooling trends emerge in the higher warming scenarios, and we investigate their drivers in North America, where the cooling magnitude exceeds 1.5°C per century. Using a method based on constructed circulation analogues, we find that the projected cooling trend is almost completely explained by thermodynamic drivers and we reconcile this finding with the model vegetation changes. Our findings serve a double purpose. On one hand, they show the significant contribution that land-use changes can have regionally for the attenuation of annual temperature maxima, supporting the case for their careful consideration in future mitigation and adaptation strategies. On the other, they highlight how highly idealised protocols like TIPMIP could bias climate projections post emissions cessation if they do not include realistic projections of land use changes.
Bibliography
Jones, Colin, Bossert, I., Dennis, D. P., Jeffery, H., Jones, C. D., Koenigk, T., Loriani, S., Sanderson, B., Séférian, R., Wyser, K., Yang, S., Abe, M., Bathiany, S., Braconnot, P., Brovkin, V., Burger, F. A., Cadule, P., Castruccio, F. S., Danabasoglu, G., … Ziehn, T. (2025). The TIPMIP Earth system model experiment protocol: phase 1. https://doi.org/10.5194/egusphere-2025-3604.
How to cite: Rivosecchi, A., Dittus, A., Hawkins, E., Schiemann, R., and Fischer, E.: Evolution of global climate and regional hot extremes following CO2 emissions cessation., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14618, https://doi.org/10.5194/egusphere-egu26-14618, 2026.
The increasing frequency of extreme hot events poses major societal and scientific challenges due to their adverse impacts on human and natural systems, compounded by their unpredictable nature. Climate models are essential for identifying the mechanisms that amplify extremes and for anticipating long-term changes that inform decision making, yet their accuracy is limited by internal variability, structural uncertainties, and systematic biases. Observational constraint approaches that link past and future behavior of physical observables offer a promising way to address these limitations, though they often rely on region-specific empirical relationships.
Here, we show that future changes in hot event probabilities and their uneven spread across global land areas depend critically on the historical properties of temperature distributions. In particular, historical variability controls the growth rates of probabilities, either amplifying or dampening the effects of regional background warming, with important implications for climate-change projections. Building on this insight, we develop a universal analytical framework that combines observational evidence with model output to provide more robust assessments of future changes. Results indicate that hot event probabilities may increase faster than suggested by models alone across much of the land surface. In large areas, including the Euro-Mediterranean and Southeast Asia, observation-constrained increases could exceed model-based estimates by nearly a factor of two, even at low levels of global warming. Surpassing the 2 °C warming threshold could push highly vulnerable regions, such as the Amazon and other tropical land areas, into uncharted climate conditions where extreme heat becomes routine.
These findings support more realistic evaluations of future risk and underscore the need for strengthened mitigation efforts to prevent rapid and potentially irreversible climate shifts.
How to cite: Simolo, C. and Corti, S.: Hot extremes increase faster than models suggest: evidence from observation-constrained projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17562, https://doi.org/10.5194/egusphere-egu26-17562, 2026.
Understanding and characterizing temperature extremes is essential for assessing climate impacts and risks. Robust statistical analysis of such extremes requires large datasets, yet observational records often provide limited samples of rare events. Hindcasts, i.e., retrospective forecast model runs for past dates, are typically used to correct model biases, but their potential for extreme event analysis remains underexplored. Approaches such as UNSEEN (UNprecedented Simulated Extremes using Ensembles) have investigated the potential of seasonal hindcast ensembles to provide large samples of events that are physically plausible, particularly for assessing rare events. However, seasonal hindcasts often focus on monthly means.
In this study, we explore whether a similar approach can be applied to subseasonal hindcasts, evaluating their potential to serve as alternative realizations of extreme events at daily resolution. We use two complementary methods to compare global temperature extremes in ECMWF subseasonal hindcast with ERA-5 reanalysis: (1) the statistical upper bound of daily 2-meter temperature, and (2) the probability of record-breaking daily 2-meter temperature. By leveraging existing subseasonal hindcast ensembles, we aim to evaluate whether these datasets can be repurposed to study temperature extremes that have not yet been observed but are plausible under current climate conditions.
How to cite: Rivoire, P., Pyrina, M., Naveau, P., and Domeisen, D.: Heat extremes in subseasonal hindcasts: a General Extreme Value perspective, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18203, https://doi.org/10.5194/egusphere-egu26-18203, 2026.
Climate extremes, such as floods, heatwaves, and droughts, have myriad impacts across natural and social systems. However, traditional methods used for monitoring impacts tend to focus on single hazards or indicators (e.g., fatalities), address only quantitative consequences (e.g., economic losses), and frequently overlook indirect and social consequences (e.g., conflicts, mental health). Here, we show how text data can be used to measure the societal impacts of climate extremes across diverse text sources, including newspapers, social media, and Wikipedia articles.
First, we analyze over 26,000 newspaper articles on the July 2021 river floods in Germany to reveal cascading impacts across sectors like infrastructure, water quality, mental health, and tourism. Second, Twitter data from the 2022 drought in Italy is used to map public concern and perceived consequences, which align with observed socioeconomic indicators. Finally, we scale our analysis globally with Wikimpacts 1.0, a database of climate impacts extracted from 3,368 Wikipedia articles covering 2,928 events from 1034 to 2024, providing national and sub-national records of deaths, injuries, displacements, damaged buildings, and economic losses.
Together, these case studies illustrate the value of text-derived impact datasets for complementing traditional monitoring approaches. We also discuss the challenges of using such datasets, including representational biases, uneven temporal and spatial coverage, and differences in how impacts are reported. We conclude by discussing how the field can move towards shared standards and best practices, enabling more comparable and transparent use of text data for monitoring the impacts of climate extremes.
How to cite: Madruga de Brito, M., Wang, J., Sodoge, J., Li, N., and Nunes Carvalho, T. M.: Enhancing impact monitoring by using computational text analyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4070, https://doi.org/10.5194/egusphere-egu26-4070, 2026.
Climate extremes exact a heavy and differential toll on society. Reported economic losses are primarily concentrated in developed economies, whereas reported fatalities occur overwhelmingly in developing economies. Moreover, even at single locations the adverse impacts of extreme climate events are often unequally distributed across the population. Understanding such impacts holds enormous societal and economic value, and is a key step towards climate resilience and adaptation. Recent research advances include improved impact forecasting and enhanced understanding of how the interaction between human and natural systems shapes the impacts of climate extremes. Nonetheless, there are some key challenges that have hindered progress. We focus on three: Limited availability and quality of impact data, difficulties in understanding the processes leading to impacts and lack of reliable impact projections. We argue that newly released datasets and recent methodological and technical advances open a window of opportunity to address several dimensions of these challenges. Notable examples include extracting impact information from textual sources using large language models and developing impact projections using data-driven approaches. Moreover, interdisciplinary collaborations between the social and natural sciences can elucidate processes underlying past climate impacts and enable building storylines of future societal impacts. We call for building momentum in seizing these opportunities for a breakthrough in the study of impacts of climate extremes. Achieving meaningful progress will require interdisciplinary and intersectoral research, and strong collaboration across academic, policy and practitioner communities.
How to cite: Messori, G., Boyd, E., Nivre, J., and Raffetti, E.: Challenges and Opportunities for Understanding Societal Impacts of Climate Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2212, https://doi.org/10.5194/egusphere-egu26-2212, 2026.
Humanitarian crises are the tip of the iceberg in climate change adaptation, yet their future is rarely quantified in human and economic terms. We use machine learning to simulate future estimates of people in need of humanitarian aid and required funding under the business-as-usual scenario (SSP2-RCP4.5) with warming of 2.1–2.4°C by 2100. Humanitarian needs rise to a baseline of 410±22 million people and USD2024 64±8 billion annually by 2050 worldwide, increases of 28% and 30% respectively compared to the current (320 million people and USD 49 billion). A lightly optimistic simulation holds needs near the current, while a medium pessimistic simulation leads to 614±68 million people and USD2024 96±19 billion by 2050, increases of 92% and 96% respectively. Our results show empirical vulnerabilities and an opportunity cost, as resources for crisis response displace funding for adaptation and mitigation. Yet, sustained investment could curb the impacts even with climate inertia.
How to cite: Jäpölä, J.-P., Berlin, A., Fabri, C., Hrast Essenfelder, A., Marzi, S., Poljanšek, K., Ronco, M., Van Passel, S., and Van Schoubroeck, S.: Future cost of climate change for humanitarian crises, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4462, https://doi.org/10.5194/egusphere-egu26-4462, 2026.
Carbon bombs comprise 425 fossil fuel megaprojects whose cumulative potential emissions exceed by at least a factor of two the remaining global carbon budget compatible with the Paris Agreement. The full exploitation of these projects would therefore generate substantial additional warming. As high-impact climate extremes intensify with each increment of warming, a central challenge is to quantify how emissions from individual projects translate into concrete physical and societal impacts across current and future generations.
Within the Source2Suffering project, we develop a modelling framework that links project-level CO₂ and CH₄ emissions to lifetime exposure to six categories of high-impact climate extremes, including heatwaves, droughts, and floods, using a storyline-based approach. The framework also quantifies each project’s contribution to committed glacier mass loss and multi-century sea-level rise. By explicitly representing uncertainties, it provides probabilistic estimates of how warming increments induced by individual fossil fuel projects propagate through physical processes to generate compound and cascading risks.
The results reveal marked spatial and intergenerational inequalities in exposure. These arise from (i) physical mechanisms that amplify extreme hazards in many regions of the Global South, and (ii) demographic trends that concentrate most of the world’s present and future population in these highly affected areas. By establishing a tractable link between specific emission sources, the physical drivers of high-impact extremes, and their long-term societal consequences, this framework contributes to the development of scientifically grounded information to support climate mitigation efforts.
How to cite: Laridon, A., Thiery, W., Pietroiusti, R., Smith, C., Rogelj, J., Zhang, J., Schleussner, C.-F., Menke, I., Zekollari, H., Schuster, L., Nauels, A., Palmer, M., and Schewe, J.: Linking Emissions from Fossil Fuel Megaprojects to Lifetime Climate Extremes Across Generations and Multi-Century Committed Change , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-537, https://doi.org/10.5194/egusphere-egu26-537, 2026.
Unclear and inconsistent terminology for high impact climate phenomena, including concepts such as tipping points, irreversibility, ‘collapse’ and ‘shutdown’, presents a substantial barrier to clear understanding of Earth system risks. These terms are frequently used in assessments of major subsystem shifts in ocean circulation, ice sheets and forest biomes, yet they are often applied without shared definitions across scientific, policy and public contexts. This inconsistency affects how scientific results are interpreted, including perceptions of how quickly changes may unfold and whether different parts of the climate system might influence one another. It also has important psychological and emotional impacts. Language that sounds dramatic or alarming may be intended to motivate action, but it can instead lead to desensitisation, message fatigue, denial or even the spread of misinformation. These reactions can weaken engagement and undermine societal preparedness for potential climate driven transitions.
Government science and policy teams, rely on clear and consistent terminology for effective decision making in situations where thresholds and impacts remain uncertain. To support this need, we – as communication specialists work extensively at the interface between science and policy - are developing an evidence-based glossary and guidance for terminology related to tipping points and other high impact climate concepts. The aim is to improve internal communication and to support clearer interpretation of scientific assessments used in national risk planning.
The project is grounded in social science and uses a mixed methods design. It began with a review of existing definitions and research on the psychological effects of climate language. We carried out semi-structured interviews and workshops with scientists and government officials, and this highlighted how linguistic ambiguity affects policy development and the evaluation of uncertain risks. Utilising ta broad cross section of Met Office staff, we carried out focus groups to explore how different definitions were perceived and understood. Participants, including those with strong scientific backgrounds, showed substantial disagreement about the meaning and implications of key terms. This indicates that confusion around terminology linked to tipping point research is not limited to public audiences but also exists within expert communities.
Insights from this analysis are guiding the co creation of a public facing glossary developed with an expert working group of twelve multidisciplinary specialists at the Met Office. Completion is planned for March 2026, alongside continued engagement with international bodies including WCRP and IPCC. By strengthening shared understanding of terms related to climate system transitions and critical thresholds, this work aims to support more coherent communication of high impact climate concepts, improve public and policy interpretation of uncertain risks and reduce unintended emotional and behavioural responses that can undermine, and distract from effective, and much needed climate action.
How to cite: Macneill, K. and Martin, L.: An Up-HILL Battle: Building consensus on terminology for high impact climate events and tipping point risks., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18736, https://doi.org/10.5194/egusphere-egu26-18736, 2026.
Isolated and compound climate extremes, such as droughts and heatwaves, are intensifying under global warming. Although recent studies have advanced the physical understanding and classification of compound events, their socioeconomic impacts remain poorly quantified at the global scale using disaster record databases. Building on evidence that compound drought–flood events can generate impacts substantially larger than those from isolated hazards, this study extends the inquiry by providing a global assessment of the socioeconomic impacts of compound drought–heatwave (CDH) events.
To achieve this, we use the Emergency Events Database (EM-DAT) for the period 1960–2025 and analyse reported drought and heatwave disasters at the global scale. CDH events are identified using complementary approaches, including overlapping drought and heatwave records within the same location (top-level administrative unit) and the “Associated Types” information in EM-DAT, thereby allowing assessment of sensitivity to event definition. Furthermore, EM-DAT drought events are compared with heatwave conditions derived from the ERA5 reanalysis to evaluate consistency between reported impacts and climatic co-occurrence. Socioeconomic impacts are quantified using the affected population, human fatalities, and reported damages.
Preliminary results show a clear increase in the number of reported areas affected by CDH events globally, particularly since the mid-2010s. Moreover, CDH events are consistently associated with greater impacts than single hazards. Specifically, using matching events within EM-DAT, compound events exhibit greater total damage, while fatalities during heatwaves increase by up to a factor of five when drought conditions co-occur. Furthermore, when drought impacts from EM-DAT are associated with heatwaves identified in ERA5, the damage and affected population are, respectively, two to four times higher than for isolated drought events.
Taken together, these findings provide global-scale evidence that co-occurring droughts and heatwaves substantially amplify socioeconomic impacts. This underscores the need to explicitly account for compound extremes in climate risk assessment, adaptation planning, and disaster risk reduction.
How to cite: Worou, K. and Messori, G.: Amplified socioeconomic impacts of compound drought–heatwave events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15041, https://doi.org/10.5194/egusphere-egu26-15041, 2026.
Most of the world's food is produced in a handful of countries, the so-called breadbaskets of the world. Due to climate change, there is an increasing risk of crop failures, due to compounding hot and dry extremes. Furthermore, certain climate drivers – through teleconnections – have shown to lead to simultaneous crop failures around the globe. This highlights the importance to understand which climate processes drive global crop yield variability. Here we show global crop yield failures (Maize, Soya, Wheat, Rice, and combined) are associated with La Nina-like sea surface temperature (SST) anomalies, using Archetype Analysis. The adverse crop-yield archetypes show simultaneous hot-dry-surface imprints across the world, highlighting these high risk crop failure scenarios are driven by climate extremes. Our results demonstrate the importance in understanding the climate drivers of global crop production, and highlights the deep uncertainty associated with a changing climate. The response of ENSO due to anthropogenic activities is not yet fully understood and climate models often inaccurately reproduce the observed La Nina trends. Thus the fact that our results indicate that simultaneous crop failures are linked to La Nina like SSTs, highlights the deep uncertainty we currently face regarding food security in the future.
How to cite: Happé, T., Hamed, R., Anderson, W., Chapman, C., and Coumou, D.: Climate archetypes of simultaneous global crop failures , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-505, https://doi.org/10.5194/egusphere-egu26-505, 2026.
Prolonged summer droughts represent a significant and growing threat across Europe, as their persistence hinders hydrological recovery and severely impacts water resources, ecosystems, and agricultural systems under ongoing climatic warming. These extended dry periods can create soil-moisture deficits, ecological stress, and amplified heat extremes. Understanding the response of multi-year droughts to different warming levels is vital for shaping both adaptation and mitigation strategies.
In this study, we investigate the behaviour and severity of the 2018-2022 European multi-year soil moisture drought across a range of climate warming levels. We apply an innovative storyline attribution approach, which enables a physically consistent comparison of the same drought sequence under different climate conditions. Specifically, we utilise spectrally nudged AWI-CM-1-1-MR, constrained to follow observed synoptic-scale circulation from ERA5, to force the mesoscale Hydrologic Model (mHM). This modelling setup allows us to specifically isolate how anthropogenic warming modifies soil-moisture deficits, without altering the real-world atmospheric conditions that triggered the drought sequence.
Under the present-day climate conditions, the 2018-2022 drought produced a soil-moisture deficit of -44 (±11.8) km3, affecting 0.63 (±0.07) million km2 (11.5% of the study area). In the absence of anthropogenic climate change (pre-industrial climate conditions), the 2018-2022 multi-year event would have shown a soil moisture surplus nearly double the magnitude of present-day losses, with drought spatial extent only about one-third of current levels. Future warming levels further exacerbate these impacts. With warming of 2 K to 4 K, the losses increase from -82 (±6.6) to -256 (±7.1) km3, while drought extent expands from approximately 16% to 43%.
Overall, our results demonstrate that rising global temperatures substantially intensify multi-year droughts by both enlarging their spatial footprint and deepening hydrological deficits. As climate warming increases the likelihood that single-year droughts transition into persistent multi-year events, the findings emphasise the urgent need for effective climate mitigation and adaptation strategies across Europe. A full version of this work is currently under review in Earth’s Future; the preprint can be accessed at https://doi.org/10.22541/au.176220208.89936181/v1 .
How to cite: Kettaren, R., Sanchez-Benitez, A., Goessling, H., Athanase, M., Kumar, R., Samaniego, L., and Rakovec, O.: Storyline-based climate attribution reveals strong intensification of 2018-2022 multi-year droughts in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-493, https://doi.org/10.5194/egusphere-egu26-493, 2026.
Antarctic sea ice has experienced small increases from 1979 to 2015, followed by an unexpectedly rapid decline reaching record-low anomalies in 2016 and 2023. The significant reduction is raising questions regarding the drivers of this decline and how the Antarctic sea ice will respond to future climate changes. Here we apply an event-based storyline approach based on a coupled global climate model (AWI-CM-1-1-MR), where the large-scale free-troposphere dynamics is constrained to ERA5 data. We focus on two multi-year sea-ice loss events, 2014–2017 and 2020–2023, to examine the response of sea ice to the observed atmospheric circulation anomalies if they occurred under different global climate backgrounds. By comparing the sea-ice response under present-day climate and projected future warm climates (+2°C, +3°C, and +4°C global mean surface warming relative to preindustrial), we separate the thermodynamic and dynamic effects of climate change and explore how the background climate state modulates the sea-ice response to wind anomalies. We find that the Antarctic sea-ice response remains surprisingly robust across this broad range of climate states, with a few exceptions where seasonal and regional deviations occur.
How to cite: Lyu, L., Sánchez-Benítez, A., Athanase, M., A. Roach, L., Jung, T., and F. Goessling, H.: Robust response of Antarctic sea ice to large-scale wind anomalies across different climate backgrounds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21435, https://doi.org/10.5194/egusphere-egu26-21435, 2026.
Posters on site: Tue, 5 May, 16:15–18:00 | Hall X5
Extreme precipitation in the Mediterranean basin emerges from a complex interaction between large-scale circulation, moisture transport and mesoscale dynamics, making the most damaging events difficult to sample in conventional climate simulations. This work presents a storyline-based framework to explore very rare and extreme rainfall under present and future climate conditions.
We apply ensemble boosting to the fully coupled CESM2 model to generate alternative realizations of the most intense precipitation events affecting the Southern Alps and the Spanish Mediterranean coast. Starting from a 35 member parent ensemble of CESM2, these occurrences are identified and resimulated through boosted ensembles, resulting in a large sets of dynamically consistent trajectories that preserve the synoptic evolution of the original event while sampling its internal variability by perturbing the initial conditions. Comparisons with ERA5 reanalysis and available observations are performed to assess the realism of the simulated circulation patterns and precipitation characteristics associated with these extreme events.
Preliminary results demonstrate that ensemble boosting successfully reproduces the temporal evolution of reference precipitation extremes, with many boosted members closely matching the timing and peak intensity of the parent events. In several cases, individual boosted realizations exceed the peak intensity of the reference simulation, revealing physically consistent more intense scenarios within the same large-scale setup. The amplification potential depends strongly on the perturbation lead time: short lead starts tend to cluster near the reference intensity, whereas longer lead times display a broader ensemble spread and occasionally generate substantially stronger or delayed rainfall peaks.
In a second step, a conditional attribution methodology is applied in which the large-scale circulation is constrained while the thermodynamic background is modified to represent different climate states. This allows us to isolate the thermodynamic contribution of climate change to extreme precipitation intensity, providing physically interpretable estimates of how much more intense these events become in a warmer climate.
By bridging weather-scale event evolution with climate-scale statistics, this approach provides new insight into the physical limits of Mediterranean extreme precipitation and offers a robust basis for assessing future extreme rainfall scenarios.
How to cite: Carniel, C. E., Knutti, R., and Fischer, E.: Separating dynamic and thermodynamic contributions in Mediterranean extreme precipitation (in a storyline approach), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2967, https://doi.org/10.5194/egusphere-egu26-2967, 2026.
Ireland’s winters are getting wetter, with more frequent heavy precipitation events increasing flooding risk across the Island. Extreme precipitation is a key driver of flooding in northwestern Europe; however, observational records are relatively short and represent only a single realisation of the climate state. As a result, they are inadequate for sampling low-likelihood, high-impact flood-relevant extreme precipitation events and for quantifying plausible maxima of such extremes. In this study, we quantify plausible maxima for flood-relevant winter precipitation under the current climate. We apply the UNprecedented Simulated Extremes using Ensembles (UNSEEN) approach to the flood-relevant winter precipitation indices (Rx1day, Rx5day, and Rx30days), using daily winter observations, the ECMWF SEAS5 seasonal prediction systems, and the CANARI Single Model Initial-condition Large Ensemble (SMILE) over the Island of Ireland. These indices are consistently derived across observations, pooled SEAS5 winter ensembles (ensemble member x lead times), and the CANARI SMILE. Model fidelity for CANARI and ensemble independence, stability, and fidelity for pooled SEAS5 are assessed to ensure that both models realistically represent extreme precipitation. Preliminary results indicate that both SEAS5 and the CANARI sample the physically plausible Rx1day and Rx5day extremes that exceed the maximum observed in the current climate, while neither system produces UNSEEN values exceeding the observed maximum Rx30day. The CANARI large ensemble passes the fidelity test without bias correction, whereas the SEAS5 passes the fidelity test after applying simple multiplicative mean scaling bias correction. For CANARI, plausible maxima are approximately 18.01% higher for Rx1day and 20.77% higher for Rx5day than observed maxima, while Rx30day plausible maxima are approximately 8.70% lower than the highest observed Rx30day. For SEAS5, plausible maxima exceed observations by approximately 3.05% for Rx1day and 17.68% for Rx5day, while Rx30day plausible maxima are approximately 17.74% lower than the highest observed. These results highlight the limitations of observational records in sampling extreme tails and indicate that CANARI SMILE captures a broader range of internal climate variability than the initialised SEAS5 seasonal prediction system. They also show that UNSEEN ensembles are more effective at sampling short-duration precipitation extremes (Rx1day and Rx5day) than longer-duration accumulation precipitation extremes (Rx30day). Our study highlights the value of combining the UNSEEN approach with both seasonal prediction systems and SMILEs to better understand unprecedented flood-relevant precipitation extremes in the current climate.
How to cite: Bile, M., Murphy, C., and Thorne, P.: Assessing the UNSEEN Flood-Relevant Winter Extreme Precipitation Over the Island of Ireland in the Present Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4864, https://doi.org/10.5194/egusphere-egu26-4864, 2026.
This study investigates the role of climate-change-driven sea surface temperature (SST) anomalies in intensifying extreme precipitation associated with Storm Boris. During the period 12th to 16th September 2024, Storm Boris produced extreme precipitation and subsequent flooding in Central Europe, recording over 350 mm accumulated precipitation in five days in parts of Austria. To assess the influence of climate-change-driven SSTs in the Atlantic, Mediterranean and Black Sea, we perform pseudo experiments, in which the SSTs of these water bodies are systematically reduced by 2 K. For that purpose, a model chain consisting of the Weather Research and Forecasting (WRF) model coupled to the Lagrangian particle dispersion model FLEXPART run with back-trajectory settings and a moisture source and transport diagnostic is utilized. The WRF model is further run with wind and pressure nudging over the entire simulation period and without nudging during the event in order to separate thermodynamic and dynamic responses. The moisture uptakes and losses of air parcels arriving in the Central European study region are traced backward in time for up to ten days, enabling the identification of the dominant moisture sources contributing to the observed extreme precipitation. Our analysis reveals the Eastern Europe land areas and the Mediterranean – where SSTs exhibited a strong positive anomaly compared to the long-term climatology – as primary moisture sources for Storm Boris. We further show that the decrease in available moisture by SST reduction in the Black Sea and/or the Atlantic is partially compensated by additional moisture uptake in the Mediterranean. Finally, we assess the thermodynamic sensitivity of mean precipitation to SST changes by comparing the simulated rainfall across different historical SST climatologies. The results indicate an average precipitation increase of approximately 3 % per Kelvin of SST warming for this event, emphasizing the contribution of climate-driven SST increases to the extreme precipitation observed during Storm Boris.
How to cite: Maier, P., Dütsch, M., Nadeem, I., Messmer, M., and Formayer, H.: The influence of sea surface temperatures on moisture sources of Central European Storm Boris in September 2024, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12603, https://doi.org/10.5194/egusphere-egu26-12603, 2026.
Recent research has considerably advanced our ability to model extreme storm surges. Nevertheless, simulating unprecedented events remains a challenge. Current large-scale storm surge studies often rely on conventional statistical approaches to extrapolate data beyond historical records. However, these approaches entail large uncertainties and lack the capacity to physically characterise individual events. Furthermore, research on unprecedented events primarily focuses on hazard magnitude, often overlooking other dimensions relevant for risk management decisions.
This study addresses these gaps by examining unprecedented storm surges at a European scale across multiple dimensions. We follow a large-ensemble approach to generate numerous alternative pathways of reality, capturing a broader range of climate variability than the observational records. By pooling ensembles from the ECMWF SEAS5 seasonal forecast and forcing the Global Tide and Surge Model (GTSM), we obtain a 525-year dataset of unbiased, independent storm surge events. This synthetic dataset enables the identification of physically plausible events beyond those found in historical records. We evaluate the dataset against reanalysis-based storm surges to uncover and characterise unprecedented events across three dimensions: magnitude, spatial extent and temporal occurrence. Understanding these different dimensions of unprecedence provides a significant advance in our knowledge of coastal flood risk in Europe and supports improved coastal flood risk management decisions.
How to cite: Benito Lazaro, I., Ward, P. J., Aerts, J. C. J. H., Eilander, D., and Muis, S.: Unprecedented storm surges across European coastlines, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12593, https://doi.org/10.5194/egusphere-egu26-12593, 2026.
Soil moisture-precipitation feedback is an important factor in the water and energy cycles, but how important is it on the time scale of an atmospheric extreme precipitation event? We are investigating this question using the example of heavy precipitation in July 2021, which led to destructive flash floods in Western Europe.
We quantify the importance of soil moisture by running a storyline simulation. We compare the precipitation simulated in the ICON-DREAM reanalysis and in our control run to counterfactual scenarios with soils dried out to plant wilting point and soils wetted to saturation. We find that saturating the soil increases precipitation by about 10% while drying the soil decreases precipitation by about 36% comparing ensemble median values.
Moisture tracking shows that one reason is that land surfaces in the vicinity of the impacted region are relevant for fueling the heavy precipitation. We find that evaporation is not limited by water availability, which explains the non-linear response in the precipitation amounts.
The changes in evaporation also affect the synoptic scale evolution of the event, which amplify the precipitation decrease in the dry scenario. Constraining the evolution of the event enough to produce the extreme of July 2021 was a major challenge of this study. The limited predictability of free forecasts conflicts with the need for enough lead time to allow soil moisture to impact the atmosphere in a meaningful way. We solve this problem by using data assimilation to constrain the large scale circulation of our global ICON simulations while disabling the assimilation within our region of interest.
Our work is part of the German Research Foundation (DFG) Collaborative Research Center 1502 DETECT. In DETECT we aim to answer the question of whether regional changes in land and water use impact the onset and evolution of extreme events. Our coarse approach to changes in water availability gives us an upper bound on changes we can expect as a result of human influence.
How to cite: Fohrmann, T., Szemkus, S., Heuser, O., Valmassoi, A., and Friederichs, P.: The influence of soil moisture on the extreme precipitation event in July 2021 in Western Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21160, https://doi.org/10.5194/egusphere-egu26-21160, 2026.
A stochastic weather generator (SWG) simulates realistic weather time series beyond the historical record by capturing the statistical properties of observed weather patterns. Here, we present a new spatiotemporal SWG, the MYRIAD Stochastic vIne-copula Model (MYRIAD-SIM), which simulates temperature, wind speed, and precipitation. MYRIAD-SIM captures both spatiotemporal and multivariate dependencies using conditional vine copulas. The simulated data enable new insights into compound climate and multi-hazard events by generating high-impact multivariate weather scenarios. For example, the triple storm sequence Dudley, Eunice, and Franklin, which impacted the UK and Europe in 2022, can be simulated as alternative triple-storm events, illustrating not only what happened but also what could have occurred under statistically plausible conditions, such as higher wind speeds or varying precipitation patterns. This study demonstrates how stochastic counterfactuals of historical events can support risk communication by framing hazards in a narrative, event-focused way rather than through abstract probabilities.
How to cite: Claassen, J., Jäger, W., de Ruiter, M., Koks, E., and Ward, P.: Using Stochastic Data to Simulate and Communicate Alternative Multi-Hazard Weather Extreme Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19875, https://doi.org/10.5194/egusphere-egu26-19875, 2026.
In the beginning of 2014, exceptionally energetic swells associated with the Hercules storm (also known as “Christina”) produced one of the most devastating coastal events ever recorded in Portugal. Between January 6th and 7th, coastal flooding affected more than 30 municipalities along the Portuguese coastline, with offshore buoys registering maximum individual wave heights and periods of 14.91 m and 28.10 s, respectively. The storm resulted in more than 16 million euros in direct damages due to overtopping and coastal flooding, while indirect losses (considering affected businesses and populations) are estimated to have reached hundreds of millions of euros. In this study, two physical climate storylines are developed to assess the impacts of a “Hercules”-like storm, at five key-locations along the Portuguese coastline, occurring by the end of the 21st century, under the combined influence of sea-level rise (SLR), projected changes in wave climate, and altered coastal morphology, while retaining the same statistical representativeness observed in 2014. The storyline approach enables a clear linkage to the original event and facilitates the assessment of future extreme events such as Hercules within the context of a changing climate, supporting decision-making by working backwards from specific vulnerabilities or decision points. Results indicate that the impacts of a future Hercules-like storm are projected to intensify, considering SLR and increases in high-percentile wave energy. Extreme coastal flooding is expected to affect 1.9 to 2.4 times more area than in 2014, resulting in 3.2 to 6.5 times more physically impacted buildings, particularly in densely urbanized coastal sectors. As coastal erosion is expected to reduce the natural protection of Portuguese sandy coastlines, the currently employed protection mechanisms will require robust adaptation measures, strategically defined to withstand long-return-period extreme events.
This work is supported by FCT, I.P./MCTES through national funds (PIDDAC): LA/P/0068/2020 - https://doi.org/10.54499/LA/P/0068/2020, UID/50019/2025, https://doi.org /10.54499/UID/PRR/50019/2025, UID/PRR2/50019/2025. The authors would like also to acknowledge the project “Elaboração do Plano Municipal de Ação Climática de Barcelos (PMACB).
How to cite: Lemos, G., MM Soares, P., Simões, R., Antunes, C., Bosnic, I., and Pinto, C.: A process-based physical climate storyline for the Hercules storm in Portugal: extreme coastal flooding under climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8241, https://doi.org/10.5194/egusphere-egu26-8241, 2026.
The Alps, with their complex topography, important geographic location and varying climatic influences have become a highly vulnerable region. Especially extreme precipitation and its associated impacts - from floods to landslides - are directly amplified by this distinct local environment.
Because observational timeseries are rather short and sample only limited locations, the impact-producing extreme tail of the precipitation distribution remains largely unexplored. In addition, the non-stationarity of the climate system makes data from a past climate less useful for gaining insights into current and future conditions. Coarse resolution global climate models can be used to produce long simulations including rare extreme events, but important processes such as topographic forcing and deep convection are poorly resolved, which limits physical interpretability. A different approach is needed to produce robust and actionable climate information on the local scales required for stress testing, early warning, adaptation and risk mitigation.
We suggest expanding the method of Ensemble Boosting into the realm of high-resolution modeling. We employ a global ICON setup with 10-20 km grid spacing with a two-way nested kilometer-scale European domain. Our initial goal is to simulate the 2013 Northern Alps flooding using ERA5 initial conditions. We asses lead time sensitivities for reinitializing simulations to optimize for variability and intensity within the boosted ensemble. We expect to produce physically consistent, interpretable and realistic storylines based on a historic extreme precipitation event in the Alps. These storylines enable us to assess driving processes and test physical limits of extreme precipitation in today’s climatic conditions.
With the current focus on a specific region and event we want to exercise a proof of concept embedded in a user-oriented framework. Next steps include producing a catalogue of extremes sampling across event types with the goal to physically constrain the extreme tail of precipitation distributions to reduce uncertainty in extreme value estimation, and to estimate return periods. Further applications of our approach could also be focused on climate projections or pseudo global warming simulations to gain insights into possible extremes in future climates.
How to cite: Roither, L., Prein, A. F., Fischer, E., and Aellen, N.: Ensemble boosting of extreme precipitation in the Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10755, https://doi.org/10.5194/egusphere-egu26-10755, 2026.
Understanding how climate change influences environmental extremes is vital for developing effective adaptation and mitigation strategies. In this study, we apply an event-based storyline approach to assess changes in accumulated precipitation associated with Storm Boris, which impacted Central Europe in September 2024. We examine both historical changes (attribution) and future projections and extend previous work by investigating the sensitivity of results to the degree of imposed dynamical constraint. Using the global CMIP6 coupled climate model AWI-CM1, we nudge simulations toward observed ERA5 winds—including the jet stream—across a range of climate backgrounds: preindustrial, present-day, and possible future states with 2, 3, and 4 °C global warming relative to preindustrial conditions. Two nudging configurations are compared: (1) a “weak constraint” configuration, in which only synoptic- and planetary-scale winds in the free troposphere are nudged, permitting some dynamical adjustment with warming; and (2) a “strong constraint” configuration, in which winds at all vertical levels and scales are imposed, thereby completely suppressing dynamical changes.
Both configurations capture the event, with stronger present-day rainfall in the strongly constrained configuration. The observed climate change between pre-industrial and present day is robust, with increases of 7% (4%) in accumulated rainfall under the weak (strong) constraint. Projections up to a 3ºC warmer climate show linear increases in the accumulated rainfall for both configurations. Beyond +3ºC, the response strongly diverges. Under weak constraint, rainfall changes at +4ºC are marginal or even mildly reduced relative to present-day, whereas the strongly constrained configuration continues to show linear increases. This divergence is linked to thermally-driven dynamical adjustments permitted under weak constraint. Whether these adjustments reflect a realistic response or methodological artifacts, and whether similar behaviour occurs in other events, remains to be explored. Our results highlight remaining uncertainties in storyline-based extreme precipitation projections, and demonstrate the importance of considering multiple possibilities.
How to cite: Sánchez Benítez, A., Athanase, M., and Goessling, H. F.: Sensitivity of Storm Boris rainfall intensification to wind nudging strength in event-based climate-change storyline simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9800, https://doi.org/10.5194/egusphere-egu26-9800, 2026.
This study investigates the impact of climate change on the extreme 2020 Meiyu over the middle and lower reaches of the Yangtze River (MLYR) through global variable-resolution ensemble subseasonal hindcasts. Results reveal that post-1980 climate change enhanced the 2020 extreme Meiyu rainfall over the MLYR region by approximately 17.19% at monthly scale, while simultaneously decreasing light and moderate precipitation frequency but intensifying heavy and extreme precipitation occurrences. Climate change intensified the low-pressure over northern China and southern China while weakening the Western Pacific subtropical high and the low-pressure over the Indian Peninsula. The circulation pattern results in significant shear between northeasterly and northwesterly winds in the southern MLYR region, contrasting with the high-pressure dominance in the northern MLYR region. This configuration suppressed convergence, vertical motion, and precipitation in the northern MLYR while enhancing these processes along its southern. Comparison between frequently re-initialized and subseasonal simulations further demonstrates that subseasonal simulations, by allowing full development of interactions between regional systems and large-scale circulation, more realistically represent climate change impacts on Meiyu season. In contrast, the frequently updated initial conditions in re-initialized simulations constrain such feedback processes. This study highlights the importance of utilizing global variable-resolution simulations at subseasonal-scale for climate attribution studies. Future studies would benefit from improved subseasonal forecasting capabilities to enhance attribution reliability.
How to cite: Xu, M. and Zhao, C.: Investigating Climate Change Impacts on the 2020 extreme Meiyu Through Global Variable-Resolution Ensemble Subseasonal Hindcasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11244, https://doi.org/10.5194/egusphere-egu26-11244, 2026.
Windstorms are one of the most damaging natural hazards in western Europe, yet large inter-model spread limits robust assessment of future frequency changes. Previous assessments have suggested an increasing frequency, however models often have equal and opposite future responses. Using a novel statistical technique to quantify trends in these damaging windstorms we show that the historical mid-latitude meridional pressure gradient explains much of the inter-model variability in projected windstorm frequency across a large CMIP6 ensemble. Constraining projections using the pressure gradient index reduces uncertainty lowers the likelihood of increasing windstorm frequency and indicates a robust decline in pan-European windstorm frequency over the twenty-first century. We present a plausible mechanism via atmosphere–ocean feedbacks important for the North Atlantic storm track and circulation. These results suggest extreme increases in windstorm frequency are unlikely, despite projected increases in storm severity, with important implications for future loss and impact assessments.
How to cite: Priestley, M., Stephenson, D., Scaife, A., and Bannister, D.: An emergent constraint for the future frequency of European windstorms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13386, https://doi.org/10.5194/egusphere-egu26-13386, 2026.
Large hail and heatwaves are among the most extreme weather phenomena, posing serious risks to human health, ecosystems, and infrastructure, while also leading to significant economic losses. However, the co-occurrence of large hail and heatwaves, and the potential physical mechanisms linking these two phenomena, remain poorly understood. In this study, we investigate the climatology of large hail and the atmospheric drivers of large hail and heatwave co-occurrences across selected European regions, using an 11-year convection-permitting climate simulation with the COSMO regional climate model (2011–2021). In addition, we assess how these extremes may evolve under future climate conditions (+3°C global warming).
Results show increases in large hail frequency across Europe in a warmer climate. In central and eastern regions, the frequency rises approximately 20 %, whereas in the Alpine, Mediterranean, and Baltic regions it nearly doubles. Exceptions are France and Spain, where large-hail frequency declines by 26% and 33%, respectively. Also, there is a notable correlation between the occurrence of heatwaves and large hail across central and eastern Europe. This relationship is less evident in southern Europe, due to large hail occurs mainly in autumn storms caused by large-scale disturbances. Additionally, large hail during heatwave days is forms in environments with higher median values of most-unstable convective available potential energy and 2 m temperature than large hail in the absence of heatwaves. A spatiotemporal analysis revealed that the days leading up to large hail events increasingly coincide with heatwave conditions. In the present climate, large hail is most often found within ~500 km of heatwave boundaries, both inside and outside them. The future climate scenario indicates a spatial shift of large hail events beyond the heatwave extent across all continental domains.
How to cite: Agayar, E., Killian, B., Thurnherr, I., and Wernli, H.: Co-occurrence of large hail and heatwaves in European regions in current and future climate scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2749, https://doi.org/10.5194/egusphere-egu26-2749, 2026.
Extreme summer heat stress presents increasing public health risks across Europe. These extremes are strongly influenced by large-scale atmospheric circulation, yet the specific pathways linking circulation evolution to surface heat stress amplification remain poorly understood. Using the simplified Wet Bulb Globe Temperature (sWBGT), which accounts for both temperature and humidity effects on heat stress, we analyze extreme summer (JJA) events during 1979–2023 based on ERA5 reanalysis and a seven-class European weather regime (WR) classification. We define extreme events as regional sWBGT exceeding the 95th percentile for at least three consecutive days. Extreme sWBGT events across Europe occur predominantly during blocking regimes, with European and Scandinavian blocking playing a dominant role in many regions. We then examine how blocking evolves prior to heat stress peaks. Results show that only Scandinavia exhibits a statistically robust tendency for blocking to develop shortly before the peak, suggesting a circulation transition preceding extreme heat stress. In contrast, most other European regions experience peak heat stress under blocking conditions that are already established several days in advance, highlighting the dominant role of persistent circulation patterns. The time interval between the onset of blocking and the heat stress peak typically ranges from 3 to 7 days. These contrasting circulation pathways are closely linked to different surface amplification processes. Circulation transitions maybe associated with rapid atmospheric adjustment and surface warming, whereas persistent blocking likely promotes the accumulation of radiative forcing and progressive soil moisture depletion. Understanding how these mechanisms vary across pathways can help explain regional differences in European heat stress extremes and may improve predictions of future events.
How to cite: Zhang, Q., Kjellsson, J., and Black, E.: Circulation pathways and surface drivers of extreme summer heat stress over Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19952, https://doi.org/10.5194/egusphere-egu26-19952, 2026.
Compound hot–dry events have recently led to severe consequences globally, often triggering cascading impacts across ecological and socio-economic systems. Currently, most analyses of hot–dry extremes rely on short observational records or projections, limiting evaluation against pre-industrial variability—the climatic range to which many natural and human systems adapted over centuries. This makes it difficult to place impacts of the increased intensity and frequency of compound events in an appropriate context for examining adaptation needs.
Here we leverage a unique data coverage in the Swiss Alps to quantify changes in summer mean climate and in compound hot–dry extremes and their associated return periods from 1600 to 2099 CE. Data used include multi-century temperature and atmospheric drought reconstructions from tree rings going back to 1600 CE, instrumental station records, and local-scale climate projections for 1981-2099.
Copula-based modelling shows that summers classified as extreme in pre-industrial conditions have become common in today's climate and are expected to correspond to cold and wet conditions by the end of the century. Our analysis further shows that the hot–dry conditions witnessed in summer 2003—characterized by simultaneous positive temperature and vapor pressure deficit (VPD) anomalies of 5.3°C and 2.6 hPa relative to the pre-industrial mean, respectively—were unprecedented over at least the past 400 years and are projected to remain rare until the end of the century under RCP2.6. By contrast, they are likely to occur every 2-3 years under RCP4.5 and even to become colder and wetter than average by 2070-2099 under RCP8.5, since in the latter case, temperature and VPD anomalies are projected to exceed pre-industrial conditions by 10.4°C and 8.1 hPa in the extreme case (30-year return period).
Without countermeasures, the consequences of these changes will include, among other things, dramatic losses in agricultural production and undesirable changes in forest ecosystem dynamics. Ultimately, our analysis suggests that rapid adaptation is necessary to avoid facing more frequent extreme heat and drought conditions than those observed under pre-industrial conditions. Under RCP8.5, in particular, socio-ecological systems will need to continuously adapt within 15 years to changes in the average climate to avoid facing high-impact hot-dry compound event frequencies higher than those experienced at any time over the past 400 years. Given that adaptation in mountain regions is currently not keeping up with the realized and projected climate impacts, as pointed out in several studies, we argue that the required speed of adaptation can pose substantial challenges for alpine societies.
How to cite: Neukom, R., Arosio, T., Bottero, A., Kempel, A., Muccione, V., Rixen, C., Treydte, K., and Calanca, P.: Hot-dry compound events in the European Alps: Multi-century assessment (1600-2099 CE) indicates the need for fast adaptation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9101, https://doi.org/10.5194/egusphere-egu26-9101, 2026.
Previous studies have argued that the upper bound of temperature extremes in mid-latitude regions is reached by minimizing near-surface moisture during high low-tropospheric temperatures. Here, we revisit these theories for the Île-de-France region using the ERA5 reanalysis and show that the highest annual temperatures occur within the moist-to-expected range of the summer (June–August) near-surface humidity distribution. However, during the most extreme events, relative humidity is minimized as soil moisture approaches the wilting point and the atmospheric boundary layer deepens. Using the statistical distributions of these indicators and their temporal evolution in ERA5, we evaluate the representation of thermodynamic drivers in selected CMIP6 large ensembles. Finally, we apply a recently published revised framework of dry convective instability to estimate maximum attainable temperatures in both ERA5 and CMIP6, highlighting how climate change may modify heatwave dynamics in the Paris region.
How to cite: Mayer, M., Parey, S., Petter, C., Thao, S., and Yiou, P.: ERA5-Based Validation of Thermodynamic Extreme Heatwave Drivers of the Paris region in CMIP6 simulations., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3579, https://doi.org/10.5194/egusphere-egu26-3579, 2026.
How atmospheric circulation patterns associated with extreme weather respond to climate change remains a challenging question. To explore this issue, we combine spectral nudging in a global climate model (AWI-CM1) with hindcasts, similar to ensemble boosting, in an event-based storyline framework. We examine the dynamic response to climate change of selected atmospheric blocking events associated with winter cold-air outbreaks and summer heatwaves in Eurasia. First, the large-scale circulation during the preconditioning phase of a blocking is constrained by spectral nudging toward reanalysis data, ensuring that the synoptic and planetary-scale environment is realistically and consistently reproduced in different climate backgrounds. The nudging is then switched off a few days before the blocking onset, allowing the model (including the atmospheric circulation) to evolve freely. We generate an ensemble with perturbed initial conditions to sample internal variability of the blocking development due to chaotic error growth. By applying this procedure under pre-industrial and +4 °C warmer climates compared to the present-day climate, we can separate the thermodynamic effects of climate change from the dynamical response, and quantify how a warming climate modifies both the evolution of atmospheric blocking (e.g., intensity and persistence) and the associated extreme weather impacts. We find that the climate state exerts a moderate and event-specific influence on blocking dynamics.
How to cite: Zhuo, W., Sánchez-Benítez, A., Athanase, M., Jung, T., and Gößling, H.: Exploring the changing dynamics of atmospheric blocking with a modified event-based storyline approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11790, https://doi.org/10.5194/egusphere-egu26-11790, 2026.
Climate change drives an increase in the frequency of multiple meteorological extreme event types (e.g., extreme precipitation, storms, droughts, heatwaves) by affecting thermodynamic and dynamic processes in the coupled land-atmosphere system. For example, the extended droughts during 2018-2020 in Europe, flooding triggered by extreme precipitation in Germany in 2021, as well as Valencia and central France in 2024, or prolonged heatwaves in 2003, 2015, 2018, and 2022 across continental Europe had strong adverse impacts on socio-economic systems and the environment. Given a higher frequency of extreme events, it becomes more likely that regions experience events of the same or different types in consecutive seasons, thereby challenging the regions’ short-term coping and recovery ability and long-term resilience.
While extreme events are generally well-studied, holistic analyses of typical sequences of extreme events are missing. Compound analyses commonly focus on specific combinations of events, but usually miss typical intra-annual sequences of extreme events with the potential for high impacts.
Our analysis addresses the question 1) which sequences of extremes occur most often, 2) how robust they are, and 3) their physical implications. We assess intra-annual sequences of extreme seasons on the European scale in a regional multi-member ensemble of the Canadian Regional Climate Model version 5 (CRCM5) covering the European CORDEX domain at a high spatial resolution (0.11°, 12 km). The CRCM5 was driven by four members of the Max-Planck-Institute Grand Ensemble (MPI-ESM-LR) under SSP3-7.0. Given that the four members differ only by initial conditions and thus share the same climate, this setup quadruples the sample size for finding extreme events. We selected extreme event indicators for extreme heat, droughts, extreme precipitation and wind. They cover hazards of regionally varying importance, but each of them poses considerable risks to human and natural systems in Europe. The sequences of extreme events were derived using the sequential pattern mining algorithm cSPADE.
In this contribution, we show first findings on the most prevalent sequences of seasonal events under SSP3-7.0. We map vulnerability hotspots associated with intra-annual extreme event characteristics and present physical “stories” corresponding to the sequences. Furthermore, we aim to provide the basis for understanding potential interrelations of seasonal extreme events.
How to cite: Böhnisch, A., Lee Newell, M., Meuriot, O., Soto Martin, J., Reiter, A. C., and Drews, M.: Emerging intra-annual sequences of climate extremes in Europe , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14884, https://doi.org/10.5194/egusphere-egu26-14884, 2026.
Many countries rely on international trade to ensure food security. With climate change and projected increases in the frequency and severity of extreme weather events, a significant portion of currently traded crops is vulnerable to climate extremes. While many studies have quantified the impact of extreme weather on crop production, few have linked these impacts to international trade and analyzed how future risks differ from the past. In this study, I combined crop modeling with FAOSTAT on crop and food trade data to identify the worst-case scenario in which extreme weather affects global staple crop trade. Six staple crops were included in the analysis. Probability distributions of each crop’s production were estimated for both historical and future periods under the 2020 crop distribution baseline. The worst-case scenario was determined based on the amount of traded crop affected in the past and future climates. The results provide insight into how future risks differ from historical patterns and whether international trade can continue to ensure food security under changing climate conditions.
How to cite: Su, H.: Identify the worst-case scenario where extreme weather has the greatest impact on the global staple crop trade, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5916, https://doi.org/10.5194/egusphere-egu26-5916, 2026.
The Bay of Bengal is one of the most densely populated regions globally, bordered by India, Bangladesh, and Myanmar. Its coastal zones represent critical hotspots from both societal and agricultural perspectives. Major river deltas, including those of the Brahmaputra and Ganges in Bangladesh, the Mahanadi in India, and the Ayeyarwady in Myanmar, provide essential freshwater resources that sustain highly productive agricultural systems and support large local populations. However, ongoing climate change is increasingly associated with extreme weather conditions, such as elevated temperatures, prolonged droughts, and intense precipitation events.
To investigate how climate change at different time horizons and levels of warming influences these extremes, we conducted five regional climate simulations using the Weather Research and Forecasting (WRF) model at 5km horizontal spacing. One simulation represents a 30-year reference period (1981–2010). Two additional simulations cover the mid-21st century (2031–2060) under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. The remaining two simulations represent the late 21st century (2071–2100) under the same SSP2-4.5 and SSP5-8.5 emission pathways.
The results indicate a substantial increase in extreme heat across all river deltas. The number of days exceeding 40 °C is projected to double under SSP2-4.5 and to triple under SSP5-8.5 by the end of the century. Drought frequency increases markedly, with the number of drought events projected to quadruple under both scenarios. Concurrently, extreme precipitation, measured by the RX5 index, shows significant increases in the Ayeyarwady and Brahmaputra deltas. The combined effects of intensified heat stress, more frequent droughts, and increasingly severe precipitation events present major challenges for both local populations and agricultural systems, potentially increasing the risk of displacement in these vulnerable regions.
How to cite: Messmer, M., González-Rojí, S. J., and Leonard, S.: Extreme weather events in agriculturally important regions in the Bay of Bengal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14525, https://doi.org/10.5194/egusphere-egu26-14525, 2026.
Climate impact assessment studies strongly depend on fine representations of meteorological fields. Downscaling addresses the trade-off between data requirements and storage capacity, yet the faithful replication of extreme-value statistics and spatiotemporal consistency presents a persistent issue. We present an efficient generative AI model for spatiotemporal downscaling. Using coarse-resolution monthly fields as inputs, the model reconstructs sequences of daily fields with the enhanced spatial resolution. The AI-generated daily fields accurately reproduce spatial coherence, temporal persistence, and extreme-value characteristics, showing strong agreement with ground-truth daily observations. We look forward to applying this framework more effectively to future studies on the impacts of extreme events.
How to cite: Huang, Y., Bathiany, S., Yang, S., Aich, M., Hess, P., and Boers, N.: Better serving impact assessments via AI: Reconstructing daily extremes from spatiotemporal downscaling of monthly fields, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8004, https://doi.org/10.5194/egusphere-egu26-8004, 2026.
Storylines, or tales of future weather, are an increasingly popular climate communication strategy. Storyline research aims to inform about how extreme events arise and how severe they may become under different background climates, connecting scientific knowledge and lived experience. Central to this approach is a focus on plausibility rather than probability. Such "what-if" scenarios can stress-test policy and infrastructure, guiding or strengthening adaptation efforts. This study presents a reproducible chain of methodological steps for constructing such tales through data mining, which is demonstratively applied to the EURO-CORDEX ensemble to produce a coherent and communicable extreme heat storyline for Belgium. We present the results from a first workshop with city officials and emergency coordinators, which successfully launched an ongoing dialogue between stakeholders and scientists about the broader use of storylines as an accessible tool for climate adaptation.
How to cite: Carlier, N.: Towards actionable storylines: development of a reproducible workflow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12831, https://doi.org/10.5194/egusphere-egu26-12831, 2026.
Posters virtual: Fri, 8 May, 14:00–18:00 | vPoster spot 4
EGU26-21797 | ECS | Posters virtual | VPS7
Heatwave and Air pollution, a synergetic effect or not: A case StudyFri, 08 May, 14:36–14:39 (CEST) vPoster spot 4
Temperature essential for all the life form, but when the same temperature crosses its threshold limit it can become a threat for the same. In the recent decades the world has experienced a shift in temperature range both minima and maxima, as both are shifting towards the higher tail. And this is detrimental for health and air quality. Also very few studies talk about how rising temperature can impact the air quality and vice versa. Therefore, in the present work we have studied the long term heatwave pattern over Delhi, India using the ground based and satellite data observations. Delhi is known for its hot summers, landlocked geography and dense population. During May 2022 , city experienced long heat wave event where daily maximum temperature observed higher than 40 OC for consecutively around 10 days which not only causes heat stress but also the pollution stress over the city as the concentration of Particulate matter (PM10 and PM2.5) observed significantly higher than the non-heatwave period of the month. Air Quality Index (AQI) was moved from moderate to very poor during the heatwave period compared to non-heatwave where AQI showed satisfactory to poor condition. Further we observed that the Temp and Demand data increases monotonically during this period from 30 °C with demand ≈4500–5200 MW to about 38 °C with demand ≈6800–7070 MW, indicating a strong positive linear response. The regression analysis showed with 1°C increase in air temperature can increase the city demand by 97MW with r=0.61. We have further calculated night vs day slope, indicate that when night stays hot (>35°C) people might be keep cooling system running more intensely or for longer hours. And each degree increase in nighttime temperature put much larger load on demand compared to same warming during the day.
How to cite: Masiwal, R., Ganguly, D., and Kunchala, R.: Heatwave and Air pollution, a synergetic effect or not: A case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21797, https://doi.org/10.5194/egusphere-egu26-21797, 2026.
