These include: (1) identifying the compounding drivers, including physical drivers (e.g., modes of variability) and/or drivers of vulnerability and exposure, of the most impactful events; (2) Developing methods to better shape the definition and classification of compound events, i.e. legitimate the ‘cut-offs’ in the considered number of hazard types or variables to ultimately disentangle enough information for decision-making; (3) Understanding whether and how often novel compound events, including record-shattering events, will emerge in the future; (4) Explicitly addressing and communicating uncertainties in present-day and future assessments (e.g., via climate storylines/scenarios); (5) Disentangling the contribution of climate change in recently observed events and future projections (attribution); (6) Employing novel Single Model Initial-condition Large Ensemble simulations, which provide hundreds to thousands of years of plausible weather, to better study compound events. (7) Developing novel statistical methods (e.g., machine learning, artificial intelligence, and climate model emulators) for studying compound events; (8) Assessing the weather forecast skill for compound events at different temporal scales; (9) Evaluating the performance of novel statistical methods, climate and impact models, in representing compound events and developing novel methods for constraining/reducing uncertainties (e.g., multivariate bias correction and observational constraints); and (10) engaging with stakeholders to ensure the relevance of the aforementioned analyses.
We invite presentations on all aspects of compound events, including but not limited to the topics and research challenges described above.
Human-induced climate change is leading to an increase in the intensity and frequency of some severe extreme weather and climate events, including compound extreme events (Seneviratne et al. 2021; Seneviratne et al., in preparation). This presentation will provide an overview of recent literature on this topic in the context of climate projections, as well as in relation to climate adaptation and mitigation. A recent study assessing projected changes in concurrent extreme events at country level at different levels of global warming reveals an increasing probability of near-permanent extreme conditions in most countries of the world with increasing global warming (Batibeniz et al. 2023). An analysis of changes in spatially compounding hot, wet and dry events with increasing greenhouse gas forcing additionally reveals that the spatial extent of top-producing agricultural regions threatened by climate extremes will increase drastically if mean global warming shifts from +1.5 ∘C to +2.0 ∘C, and possibly higher levels of global warming (Biess et al. 2024). Some compounding changes also come from the clear increase in the number of extreme events affected by human-induced climate change, as recently shown for heatwaves on global scale (Quilcaille et al. 2025). Further new results highlight how changes in climate extremes and compound events constrain potential future options for climate mitigation and adaptation, and why they need to be integrated in the development of plausible emissions and adaptation scenarios for the coming decades.
References:
Batibeniz, F., M. Hauser, and S.I. Seneviratne, 2023: Countries most exposed to individual and concurrent extremes and near-permanent extreme conditions at different global warming levels. Earth Syst. Dynam., 14, 485–505, 2023 https://doi.org/10.5194/esd-14-485-2023
Biess, B., L. Gudmundsson, and S.I. Seneviratne, 2024: Future changes in spatially compounding hot, wet or dry events and their implications for the world’s breadbasket regions. Environ. Res. Lett. 19, 064011, https://doi.org/10.1088/1748-9326/ad4619.
Quilcaille, Y., L. Gudmundsson, D.L. Schumacher, T. Gasser, R. Heede, C. Heri, Q. Lejeune, S. Nath, P. Naveau, W. Thiery, C.-F. Schleussner, and S.I. Seneviratne, 2025: Systematic attribution of heatwaves to the emissions of carbon majors. Nature, https://doi.org/10.1038/s41586-025-09450-9.
Seneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513–1766, doi:10.1017/9781009157896.013.
Seneviratne, S.I. et al., in preparation: Extreme climate events from past to future: A 5-year update since the IPCC AR6 report. Manuscript in preparation.
How to cite: Seneviratne, S. I., Batibeniz, F., Biess, B., Schöngart, S., Schumacher, D., Bauer, V., Gudmundsson, L., Hauser, M., Hirschi, M., Quilcaille, Y., Seeber, S., and Windisch, M.: Compound extreme events in a warming climate: Implications for climate change adaptation and mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14314, https://doi.org/10.5194/egusphere-egu26-14314, 2026.
Spatially synchronized drought or flood events, that is the co-occurrence of drought/flood in multiple locations, can have severe impacts that challenge water and emergency management because they require resources in multiple places at once. Climate change can affect the frequency of such compound events because of its influence on drought and flood generation processes. While the impacts of climate change on local hydrological extreme events are well studied, its impact on event synchronicity remains uncertain. Here, we investigate how hydrological drought and flood synchronicities have changed in Europe during the period 1981-2020 using observations from 4299 streamflow stations. Our results show that drought synchronicity has grown significantly, most strongly in Central Europe, and that years with spatially extensive drought tend to follow one another. In contrast, flood synchronicity has remained relatively stable. Regionally, regions of growing drought synchronicity show decreasing flood synchronicity, and vice versa. Synchronicity trends are mostly in line with those of local frequencies suggesting that trends in synchronicity are mainly driven by overall frequencies, rather than by the spatial distribution of events. The observed growth in drought synchronicity highlights the need to develop adaptation measures to more frequent large-scale droughts.
How to cite: Brunner, M. I., Berghuijs, W. R., Janzing, J., and Bruno, G.: Hydrological drought but not flood synchronicity increases over Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4770, https://doi.org/10.5194/egusphere-egu26-4770, 2026.
Extreme humid heatwaves (HS) have emerged as one of the most threatening compound disasters under climate change, posing severe risks to human health and socio-economic security. Yet their dynamic evolution and mechanism, especially the relation with antecedent precipitation, remain insufficiently understood. Based on global reanalysis data from 1985 to 2024, this study presents a systematical assessment for the spatiotemporal evolution of humid heatwaves, their thermodynamic drivers, and the modulation effects of preceding precipitation. Results reveal a significant intensification trend in HS frequency, duration, and intensity, which can be statistically significantly attributed to anthropogenic forcing. The occurrences of extreme humid heatwaves are mainly driven by humidity anomaly in 94.45% of global land areas, while the influences of temperature and humidity changes on HS trends exhibit larger spatial heterogeneity. The relations between antecedent precipitation and subsequent HS are strengthening, evidenced by their increasing synchrony and high HS triggering probability. Moreover, HS exhibit distinct patterns based on preceding precipitation: HS following light precipitation are most frequent, while long-duration or heavy precipitation are likely to trigger most intense HS. Notably, HS tends to occur more rapidly after the cessation of long-duration heavy rainfall, demonstrating a differentiated regulatory mechanism from land-atmospheric coupling and necessitating context-specific adaptation strategies tailored to these divergent precipitation-HS relationships.
How to cite: Fang, J. and Tu, Y.: Increasing risk of global compound humid heatwaves and the impacts of antecedent precipitation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4325, https://doi.org/10.5194/egusphere-egu26-4325, 2026.
Compound drought-heatwave events (CDHEs), defined by the co-occurrence of soil moisture droughts and heatwaves, are among the most damaging climate extremes due to their impacts on ecosystems, agriculture, and humans. Previous studies have reported increasing CDHE occurrence in many regions. However, the extent to which CDHE trends are driven by long-term changes in the soil moisture–temperature (SM–T) feedback remains unclear, compared to their roles in single heat or drought events alone. In particular, traditional correlation-based approaches to quantify SM–T feedback are limited in their ability to resolve its causal roles.
We investigate how land–atmosphere feedback drives CDHEs using the normalized non-stationary Liang–Kleeman information flow. This framework allows us to quantify the strength of the coupling in both directions of the soil moisture–temperature feedback while considering common confounders to assess how these couplings have evolved over recent decades at the global scale. Using ERA5 and ERA5-Land, we find that the widespread increases in CDHE frequency cannot be fully explained by changes in heatwave or drought frequency alone. We identify significant trends in the coupling strength for directions of the SM-T feedback, with generally stronger trends in regions with higher water availability.
We further combine this causal analysis with anthropogenic attribution to disentangle the respective roles of anthropogenic forcing and natural climate variability, using an ensemble of CMIP6 models under historical and natural-only forcings. We find significant effects of anthropogenic emissions on CDHE frequency across most land areas. On the contrary, we find heterogeneous spatial patterns of the anthropogenic impact on the frequency of univariate extremes, emphasizing the need to investigate anthropogenic impacts on the dependence between climate variables. We therefore investigate and detect anthropogenic influences on the trends of the SM-T feedback across most land areas for both coupling directions, highlighting the role of evolving land–atmosphere feedbacks in shaping compound event likelihood. Our study provides a physically interpretable and reanalysis-based pathway toward improved understanding of compound extremes under ongoing climate change using causal, multivariate methods for identifying the compounding physical drivers of high-impact climate events.
How to cite: Therville, T., Hagan, D., and Tabari, H.: Drivers of compound drought-heatwave events: assessments of univariate extremes and causal soil moisture-temperature feedback, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13090, https://doi.org/10.5194/egusphere-egu26-13090, 2026.
Weather extremes are becoming more frequent and intense across Europe, as climate change transforms once-rare events into more common and severe occurrences with major consequences for society, calling for revised adaptation and mitigation strategies. When extremes such as terrestrial heatwaves and droughts occur in combination (CDHWs), their compound effects may lead to amplified impacts, creating complex, multiscale challenges. At the same time, marine heatwaves (MHWs) are rising in intensity, duration, and frequency, profoundly affecting marine ecosystems and showing potential relationship to terrestrial extreme weather. In Europe, both oceanic and land-based heat extremes display parallel warming trends, underscoring the connectivity of Earth’s subsystems, yet the regional teleconnections that drive this connectivity remain insufficiently explored. Understanding the relationship among heatwaves, droughts, and MHWs requires robust detection and characterisation of compound events, drawing on statistical, empirical, high-dimensional, and network analysis methods. Within the ESA XHEAT project, we are leveraging Earth Observation data to expose common spatiotemporal signatures of these extremes and to test the hypothesis that North Atlantic MHWs modulate the persistence and intensity of terrestrial heatwaves and droughts, focusing on the Iberian Peninsula, the Mediterranean basin, and Scandinavia. Early results reveal coherent patterns that suggest strong linkages between oceanic heat extremes and concurrent atmospheric extremes, supporting improved probabilistic seasonal forecasting. In addition, we are integrating machine learning techniques into traditional MHWs detection workflows to develop a mechanistic, spatiotemporal approach that captures the connectivity of these anomalies. Our work aims to enhance the understanding of how ocean–atmosphere interactions contribute to interconnected risks, enabling better prediction of such events, anticipating their impacts and promoting timely response measures to mitigate them, and thus aiming to support improved preparedness and resilience in Europe.
How to cite: Silva, F., Oliveira, A., Lopes, B., Paixão, J., Baeta, R., Barros, L., Girão, I., Cunha, R., Garcia, T., Lourenço, A., Kristiansen, J., Yang, C., Bartucca, C., Martins de Araujo, J., and Riaz, A.: Connecting Marine and Terrestrial Extremes: Oceanic Drivers of Temperature and Precipitation in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13419, https://doi.org/10.5194/egusphere-egu26-13419, 2026.
Water supply systems face their greatest challenge when stream flows decline while demand surges during extreme heat. When heatwaves coincide with low stream flows, low flows diminish thermal capacity and wetted surface area, amplifying thermal sensitivity to atmospheric forcing. The resulting impacts cascade across multiple sectors: elevated river water temperatures stress aquatic ecosystems beyond critical thresholds, thermal power plants struggle to access adequate cooling water during peak energy demand, concentrated pollutants in warm, stagnant water degrade drinking water quality, and irrigation withdrawals intensify competition among agricultural, industrial, and municipal users.
Low flow data traditionally inform how much water stakeholders can safely withdraw for domestic use, industry, agriculture, and energy production while preserving river ecosystems. However, examining low flows in isolation fails to capture how concurrent extreme heat amplifies these stresses and triggers cascading failures across interconnected water-dependent systems.
Here we quantify the spatial and temporal evolution of compound low-flow—heatwave events across European rivers from 1960–2020 using observed air temperature and high-resolution pan-European hydrological reanalysis data. We identify regional hotspots characterized by the most frequent, longest, and most intense compound events and assess changes in event frequency, duration, and intensity between historical (1961–1990) and recent (1991–2020) climatic periods. We further analyze the dominant drivers of these changes across different European regions, including increases in the number and duration of low-flow events and the frequency of heatwaves occurring during low-flow periods.
Our analysis reveals that Central and Eastern Europe exhibit the most pronounced increases in compound event frequency, duration and severity, potentially experiencing the largest impacts from these events. We find a marked escalation in compound event severity, with heatwaves increasingly coinciding with low-flow conditions in recent decades. Critically, the longest-duration compound events—which pose greatest risk to aquatic ecosystems and water-dependent economic activities—have become significantly more frequent in recent decades. These results reveal expanding spatial coverage of simultaneous low flow and heatwave hazards, with implications for water resource management under continued warming.
How to cite: Dengri, A. and Greve, P.: More frequent and intense compound low-flow and heatwave events in European rivers since 1960, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17822, https://doi.org/10.5194/egusphere-egu26-17822, 2026.
The anthropogenic shifts in the climate have triggered an unprecedented rise in climate extremes which have impacted millions of lives and caused trillions of dollars’ worth of damage. The climate drivers that cause these high impact events are usually spatially or temporally compounded. These compound climate extremes are extreme events that occur simultaneously, in close succession or due to drivers that are not implicitly extreme but become extreme when combined. The impact depends on the vulnerability and exposure of the stakeholders that define the risk. Compound climate extremes are exemplified through hot-dry such as compounding heatwaves and drought or hot-wet extremes such as compounding heatwaves and extreme precipitation. Pakistan is not a new to the occurrence of heatwaves and extreme precipitation however, the compounding of these extremes is a relatively novel field of study.
Pakistan’s climate adaptation and disaster management strategies predominantly focus on these extremes in isolation while compound climate extremes have been overlooked. To assess the scientific gap, we quantified the historical occurrences and intensities of these compounding extremes, we assessed sequential heatwaves and extreme precipitation in Pakistan from 1980 to 2024 over 47 meteorological stations across the country by employing daily observational datasets for daily maximum temperatures (˚C) and precipitation (mm).
The analysis recorded a total of 599 events for the study period. A rise in the frequency of these compounding extremes was recorded since 1980 for extreme precipitation following heatwaves events within 7 days, 5 days, 3 days and 1 day. These events are consolidated in the North and Northeastern stations of Pakistan. The highest duration for these events is recorded for 7 day interval event.
These compounding extremes are especially high risk as compared to isolated extreme events because of the smaller gap between their occurrences which leaves little to no time to respond. Moreover, these events not only cause heatwave associated morbidities and losses and damages but also lead to pluvial and flash flooding. The occurrence of these events especially in southern provinces, as depicted by the study, highlights the potentially high risk of impact as a consequence of the large population, underdevelopment, pervasive poverty, social inequalities, and crippling infrastructure which increases the exposure and vulnerability of the people to such events.
How to cite: Ijaz, S., Ullah, A., Ahmad, R., Khan, M. S., and Saeed, F.: Historical Evidence of Compound Heatwave and Extreme Precipitation in Pakistan, 1980-2024, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21490, https://doi.org/10.5194/egusphere-egu26-21490, 2026.
The sequence of compounding winter storms of 2013/14 in the United Kingdom (UK) caused a range of significant impacts across the country, totalling an economic cost of approximately £1.3 billion (Environment Agency, 2016). Heavy rain, totalling 545mm over the season, caused widespread flooding, and coastal impacts were exacerbated by high spring tides and strong winds. The Somerset Levels particularly felt the impact of the flooding, accounting for 30% of the total UK area of flooded agricultural land. This event is a case study for the COMPASS project, where the main goal is to produce a flexible and harmonised methodological framework for such compound extremes with a focus on impact attribution.
Using the flood model SFINCS (Super-Fast Inundation of Coasts), developed by Deltares, we model the total flood extent for a small region of the Somerset levels for the season. We drive this model with both factual and counterfactual (“natural”, with anthropogenic warming removed) simulations of the winter precipitation, using the HadGEM3-A large-ensemble attribution runs (Ciavarella et al., 2018). We find the likelihood of the magnitude of the observed flood extent to be 1.21 times more likely due to climate change, based on return periods. We also find that a flood event under “natural” forcing but with the same return period as the factual event would be slightly less severe in its extent, 113.40km² compared to 114.02km².
Although these results are not statistically significant, this agrees with generally inconclusive results from other studies on the 2013/14 UK winter storms, such as that of Schaller et al. (2016) who found a small, non-significant increase due to climate change in the number of properties impacted by flooding in the Thames river catchment. Potential modelling improvements to refine results for Somerset include increasing resolution and adding flood defences to better represent the coastal inundation. Investigation of attribution of the flood extent to post-industrial sea level rise also opens another avenue for exploring the compound nature of the event.
The Horizon-Europe COMPASS project (Compound events attribution to climate change: towards an operational service) is exploring climate and impact attribution of different complex extreme events, and scoping an operational attribution service. It aims to develop transferable attribution methods for operational attribution of compound extremes to support climate change evidence and policy.
How to cite: Matthews, E., Munday, G., Perks, R., Cotterill, D., Bernie, D., Couasnon, A., and Vertegaal, D.: Climate change attribution of the compound 2013/14 winter storms flooding in Somerset, UK, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7906, https://doi.org/10.5194/egusphere-egu26-7906, 2026.
Access to water is crucial for all aspects of life. Anthropogenic global warming is projected to disrupt the hydrological cycle, leading to water scarcity. However, the timing and hotspot regions of unprecedented water scarcity are unknown. Here, we estimate the Time of First Emergence (ToFE) of droughtdriven water scarcity events, referred to as “Day Zero Drought” (DZD), which arises from hydrological compound extremes, including prolonged rainfall deficits, reduced river flow, and increasing water consumption. Using a probabilistic framework and a large ensemble of climate simulations, we attribute the timing and likelihood of DZD events to human influence. Many regions, including major reservoirs, may face high risk of DZD by the 2020s and 2030s. Despite model and scenario uncertainties, consistent DZD hotspots emerge across the Mediterranean, southern Africa, and parts of North America. Urban populations are particularly vulnerable at the 1.5 °C warming level. The length of time between successive DZD events is shorter than the duration of DZD, limiting recovery periods and exacerbating water scarcity risks. Therefore, more proactive water strategies are urgently needed to avoid severe societal impacts of DZD.
How to cite: Franzke, C. and Ravinandrasana, V.: The first emergence of unprecedented global water scarcity compound extremes in the Anthropocene, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2293, https://doi.org/10.5194/egusphere-egu26-2293, 2026.
Recent studies have revealed strong trends in humid heat, including the nearing of human physiological limits in some regions. Understanding of past extremes and their meaningfulness for contextualizing future possibilities, especially in the near-term, is limited by the absence of a global analysis focused on the most extreme humid-heat-anomaly events. Here we identify record-setting humid-heat days for 216 global regions and assess the likelihood of these records being broken even under present-day climate forcing. We use several reanalyses as a historical catalogue, and large climate-model ensembles to represent other statistically plausible events. Unlike the spatial pattern of large temperature anomalies, we find that humid-heat anomalies are most intense, and most seasonally and interannually concentrated, in the deep tropics and arid subtropics. Many top events have attracted little if any prior attention. The eastern United States is especially susceptible to record-breaking humid heat due to modest current records (>1% inferred annual exceedance probability) contrasting with numerous simulated large-anomaly days. Australia and eastern China are also prone to locally exceptional episodes, with >40% of ensemble members simulating events exceeding the ERA5-based distribution maximum. Model biases for key characteristics, together with the observed record-setting day affecting its estimated return period by >2.5x in half of regions, underline several valuable aspects of a joint observation/model perspective on humid heat. This approach aids in evaluating the plausibility of as-yet-unseen extremes; identifying regions of concern that might otherwise be overlooked and underprepared; and gauging regionally specific correlations between event magnitudes and societal impacts.
How to cite: Thompson, V., Raymond, C., Suarez Gutierrez, L., and van der Wiel, K.: Distinct Favoured Regions for Historical Record-Setting and Future Record-Breaking Humid Heat, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17662, https://doi.org/10.5194/egusphere-egu26-17662, 2026.
Compound hydroclimatic extremes, particularly dry-to-wet transitions, represent a growing climate risk due to their cascading impacts on flooding, agriculture, and water resources. Under climate change, shifts in the frequency and severity of such compound events are expected, yet large uncertainties remain in their detection, characterization, and future evolution. This study presents a probabilistic, ensemble-based assessment of compound dry-to-wet events across Pakistan, with explicit attention to event detection, severity classification, and uncertainty in climate projections. Compound dry-to-wet events are detected using the Standardized Precipitation Evapotranspiration Index (SPEI), capturing transitions from sustained dry conditions to subsequent wet extremes. To systematically characterize event severity, we develop a compound magnitude index that integrates the severity and duration of both the dry and wet phases of each event. This index enables the classification of compound dry-to-wet events into mild, moderate, severe, and extreme categories, facilitating robust comparisons across regions, models, and emission scenarios. The analysis is based on historical and future simulations from CMIP6 global climate models and CORDEX dynamically downscaled regional climate models under multiple Shared Socioeconomic Pathways (SSPs). Changes in the frequency, duration, intensity, and severity distribution of compound dry-to-wet events are evaluated relative to a historical reference period. Probabilistic metrics are used to quantify ensemble agreement and spread, while uncertainty is decomposed into contributions from model structure, scenario choice, and internal climate variability. Differences between CMIP6 and CORDEX ensembles are further examined to assess the role of regional downscaling in representing compound event characteristics. Results indicate an increased likelihood and severity of compound dry-to-wet events under higher-emission scenarios, with pronounced spatial heterogeneity across Pakistan. In particular, severe and extreme events show more robust increases than mild and moderate events. Model uncertainty dominates projections of compound event magnitude, while scenario uncertainty becomes increasingly important toward the late 21st century. Regional climate models enhance the representation of localized extremes but exhibit larger inter-model variability. This study advances compound event research by introducing a SPEI-based compound magnitude framework and a comprehensive uncertainty assessment, providing valuable insights for climate risk assessment and adaptation planning in climate-vulnerable regions.
How to cite: Imran, A.: Probabilistic Changes of Compound Dry-to-Wet Events: Detection and Uncertainty from Climate Model Ensembles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23179, https://doi.org/10.5194/egusphere-egu26-23179, 2026.
European countries share essential firefighting equipment and personnel to manage wildfires. Nevertheless, climate change is increasing the likelihood of synchronous fire danger—periods when wildfire conducive weather conditions occur simultaneously across multiple European regions. Synchronous fire weather conditions could strain existing resource sharing plans and overwhelm firefighting capacities. To ensure an effective wildfire response in a warming climate, it is essential to understand how climate change influences the occurrence and spatial scale of synchronous fire danger.
Here, we analyze future fire weather synchronicity across ten European regions using the Canadian fire weather index (FWI) with three complementary perspectives—regional, inter-regional, and continental. Our analysis is based on a regional Single Model Initial condition Large Ensemble (SMILE), i.e. the CRCM5-LE, spanning the period from 1990 to 2099. This enables a robust quantification of both internal variability and forced response, as well as a sufficient sampling of extreme events. To identify impact-relevant fire weather conditions, we derive regional thresholds of FWI anomaly according to its cumulative distribution function (CDF) on burned area during 2001-2020, using CERRA reanalysis data and FireCCI burned area observations. Thereby, we focus on two levels of fire danger: moderate (FWI anomaly corresponding to 50% of burned area) and extreme (FWI anomaly corresponding to 90% of burned area). We then apply these regional thresholds to the FWI anomaly from the CRCM5-LE ensemble for each grid cell within the respective region. To quantify inter-regional and continental synchronicity, we compute weekly block maxima of the regional land area exceeding these thresholds as a proxy for regional fire danger.
From a regional perspective, we find that the magnitude (i.e., spatial extent) of fire danger increases with increasing global warming level (GWL) in all ten European regions. The increase is larger for extreme fire weather conditions than for moderate fire weather conditions. We find the strongest increases (fivefold) in the magnitude of extreme conditions in France and the Alps under 4 °C GWL. From an inter-regional perspective, we find an increasing pair-wise dependence of fire danger between regions under climate change, for both the moderate and extreme conditions. France, the Alps and Central Europe will become strongly dependent on each other in their weekly fire danger under 4 °C GWL. All region pairs show an emergence of synchronous fire danger between 2 and 3 °C GWL compared to 1990-2019, with southern regions emerging earlier (or at lower GWLs) than northern regions. From a continental perspective, we find that increasing GWLs also increases the odds of more than five European regions co-experiencing fire danger in one week, with an even stronger increase for extreme than moderate conditions.
Our results point toward increasing fire weather synchronicity in Europe under climate change and underscore the urgency to adapt current fire management strategies and collaboration in a warming climate. This is especially relevant for France, the Alps and Central Europe, that have historically low wildfire activity but will undergo a strong increase in fire danger under climate change.
How to cite: Li, X., Wood, R., Miller, J., and Brunner, M.: European fire weather synchronicity under climate change: three perspectives from regional to continental scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3975, https://doi.org/10.5194/egusphere-egu26-3975, 2026.
Urban populations are frequently exposed to complex mixtures of air pollutants, a critical public health challenge as compound exposures often produce nonlinear, synergistic health impacts greater than the sum of individual risks. This study presents a high-resolution, satellite-based assessment of population exposure to concurrent exceedances of multiple air pollutants in Gujarat’s major metropolitan areas—Ahmedabad, Surat, Vadodara, and Rajkot—from 2019 to 2024.
We leverage advanced remote sensing data from the TROPOspheric Monitoring Instrument (TROPOMI) on Sentinel-5P and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua to monitor key pollutants, including Aerosol Optical Depth (AOD), Nitrogen Oxides (NOx), Sulfur Dioxide (SO2) and Methane (CH4) as a proxy for particulate matter. By analyzing spatiotemporal patterns, we identify and characterize episodic events where multiple pollutants simultaneously exceed baseline thresholds, creating potential ‘pollution cocktails’.
These multi-pollutant exceedance events are then integrated with high-resolution gridded population data to quantify the number and demographic distribution of residents exposed to compounded air quality risks. The methodology enables a shift from single-pollutant monitoring to a holistic exposure assessment framework.
Preliminary findings reveal significant temporal and spatial heterogeneity in compound exposure events, strongly influenced by urban form, industrial activity, and meteorological conditions. The analysis identifies recurring pollution hotspots and temporal patterns (e.g., seasonal, episodic) where populations face elevated health risks from concurrent pollutants. The results underscore that mitigation strategies focused on single pollutants may underestimate population health risks in these urban centers.
This study provides a critical evidence base for designing targeted, health-centric air quality management policies. By mapping compound exposure risks, it empowers urban planners and public health officials in Gujarat to prioritize interventions, optimize monitoring networks, and develop early warning systems that address the real-world, multi-pollutant environments experienced by urban populations, thereby strengthening resilience and advancing sustainable urban development goals.
Key Words: Compound Risk, Air Pollution, Satellite Data, Population Exposure
How to cite: Gadekar, K. and Kandya, A.: Beyond Single Pollutants: Quantifying Urban Population Exposure to Concurrent Air Pollution Hazards in big cities of Gujarat, India , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1252, https://doi.org/10.5194/egusphere-egu26-1252, 2026.
Climate change is intensifying the frequency, severity, and interactions of extreme heat, drought, wildfires, and air pollution, increasing risks to both ecosystems and human populations worldwide. These risks can emerge from the accumulation of multiple hazards over time (multi-hazard risk), but also from their simultaneous co-occurrence (compound events). Here, we present a global, spatially explicit assessment of human risk from wildfires and air quality associated with hot and dry extremes, explicitly integrating multi-hazard and compound risk representations, following a hazard, exposure, and vulnerability perspective.
Using global datasets at 0.75° spatial resolution for the period 2003–2022, hazards are quantified based on the number of hot days per month (derived from exceedances of daily maximum temperature above the 90th percentile of a 1991–2020 climatology), drought occurrence (as depicted by the 6-month Standardized Precipitation–Evapotranspiration Index, SPEI), wildfire activity (characterized using MODIS Fire Radiative Power, FRP), and the number of days with PM2.5concentrations exceeding World Health Organization air quality thresholds. Human exposure is represented exclusively by gridded population density, while vulnerability is characterized using indicators capturing human sensitivity and adaptive capacity, e.g., the Human Development Index (HDI) and Water Stress Index (WSI).
Human risk is quantified by combining hazard intensity, population exposure, and vulnerability, following both a multi-hazard and a compound formulation. In a multi-hazard formulation, hazards are aggregated without requiring temporal co-occurrence, capturing the cumulative burden of climate extremes. In parallel, compound risk is assessed by explicitly accounting for the co-occurrence of hazards within the same temporal windows, enabling a direct comparison between cumulative and compound representations of risk. In addition, we quantify the global population affected by different risk classes. Our estimates indicate that approximately half of the world’s population is currently exposed to high to very high risk, while a substantially smaller fraction resides in low or extremely low risk conditions. High and very-high risk classes together account for several billion people, underscoring the widespread nature of climate-related human risk. When aggregated at the country level, risk levels exhibit a clear socioeconomic gradient, with higher average risk values concentrated in lower-income countries, low life expectancy at birth, and high infant mortality rate.
The results illustrate how a compound events perspective can alter the spatial distribution and relative intensity of human risk compared to a multi-hazard one, highlighting regions where hazard interactions may further amplify societal impacts. This work provides a generalized framework for global human risk assessment, offering new insights into how different representations of climate extremes shape risk patterns and supporting the development of more effective adaptation and risk reduction strategies.
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. This work was performed under the scope of project https://doi.org/10.54499/2022.09185.PTDC (DHEFEUS) and the Horizon Europe research and innovation programmes under grant agreement number 101081661 (WorldTrans).
How to cite: Bento, V. A., Köberle, A. C., Trigo, R. M., Lima, D. C. A., and Russo, A.: Global patterns of human risk from hot and dry climate extremes, wildfires and poor air quality: insights from multi-hazard and compound analyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6731, https://doi.org/10.5194/egusphere-egu26-6731, 2026.
In May 2023, two severe hydrometeorological events affected Emilia-Romagna (Italy), on May 2–3 and May 16–17, respectively. These events led to widespread, concurrent flooding across multiple river basins, triggered by levee overtopping and embankment failures, and impacted 37 municipalities throughout the region. This study presents a compound-event analysis of the two events, employing a dual methodological framework. First, a bottom-up, impact-based assessment was conducted, starting from documented damages and tracing back to the underlying meteorological drivers. Subsequently, a top-down, model-based analysis was performed to investigate the dynamics of these events and quantify the relative contributions of hydrometeorological and geomorphological factors to the flood events. In addition, a sensitivity analysis was conducted to assess the influence of the considered forcings and parameter choices on the robustness of the results. This integrated framework provides new insights into the dynamics and drivers of compounding flood hazards.
How to cite: De Michele, C., Banfi, F., and Russomando, M. P.: The May 2023 Flood Events in Emilia-Romagna, Italy: A Compound-Event Perspective, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17670, https://doi.org/10.5194/egusphere-egu26-17670, 2026.
Compound Heatwaves (CoHots), characterized by persistent day-night combined high temperatures, have intensified in recent decades, posing growing threats to human health, productive activities, and socioeconomic systems. Although much research has focused on the evolution of CoHots, high-resolution mapping of their changes in large metropolitan areas remains limited by sparse observational networks and coarse-resolution reanalysis data. Additionally, the influence of urbanization on the onset timing of CoHots has received little attention.
This study compares the start dates of CoHots across more than 700 urban–rural station pairs worldwide, revealing a significantly earlier onset in urban areas. Using machine learning and SHAP interpretability analysis, we demonstrate that this effect is primarily driven by urban building volume and height, rather than by the fraction of impervious surfaces. The influence is further amplified in climates with warm nights and strong daytime solar radiation.
To quantify urbanization's impact at a spatially and temporally continuous scale, we developed the Urban-informed Heatwave Ensemble AI Downscaling (U-HEAD) framework. This model integrates dynamic urbanization factors through an ensemble machine learning approach to downscale 0.25° ERA5 reanalysis data to 1 km resolution. Compared to the original product, U-HEAD substantially improves the simulation of spatiotemporal patterns and long-term trends of compound heat events. The framework can also be integrated with statistical downscaling methods to generate future high-resolution projections of CoHot evolution under combined climate change and urbanization scenarios. This research provides a robust, high-resolution modeling tool to quantify urbanization’s role in shaping compound heat extremes. Future work will focus on applying U-HEAD to project CoHot risks under various climate and urban development pathways, and to inform climate-resilient urban planning and heat adaptation strategies.
How to cite: Ji, P.: Harnessing machine learning for quantifying and attributing compound heatwave changes in metropolis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16419, https://doi.org/10.5194/egusphere-egu26-16419, 2026.
Global warming has intensified the frequency of synchronous extreme climate events, posing severe threats to the water-food-energy-ecosystem nexus and challenging regional sustainability. Current studies overlook the inverse symbiotic relationship of the droughts and wet events and the complex, nonlinear spatiotemporal correlations underlying transregional extreme climate events. Here, using complex network, we systematically identify the synchronous structure of drought, pluvial, and drought–pluvial dipole (DPD) events within the Western Route of the South-to-North Water Diversion Project in China. Our analysis reveals a distinct wet-dry co-variability between the Yangtze and Yellow River basins. From the perspectives of atmospheric circulation and local weather systems, we elucidate the physical coupling between extreme hydroclimatic events and circulation anomalies as well as moisture transport pathways. We identify remote coupling zones of DPD events and highlight a pronounced spatial asymmetry in cross-basin hydroclimatic behavior. Drought and pluvial synchronicity is predominantly characterized by short-to-medium spatial scales, compared to DPD events exhibiting robust cross-basin teleconnections. Notably, the signal sources for these extremes are anchored in the southwestern portion of the study area. We show that positive geopotential height anomalies, airflow subsidence, and monsoon disruption drive drought conditions, whereas the transport of warm, moist air generates pluvial events -together forming a “drought-pluvial seesaw” at the climatic scale. This study provides critical scientific foundation for cross-basin water resource management and offer vital insights for developing climate-resilient infrastructure and optimizing adaptive spatial planning under a changing climate.
How to cite: Li, W. and Xie, J.: Climate Network-Based Synchronized Structures Identification of Extreme Droughts and Pluvials in Cross-basin Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4417, https://doi.org/10.5194/egusphere-egu26-4417, 2026.
The concurrent occurrence of temperature and precipitation extremes, known as compound temperature-precipitation extreme events (CTPEEs), leads to more pronounced consequences for human society and ecosystems than when these extremes occur separately. However, such compound extremes have not been sufficiently studied, especially during boreal spring. Spring is an important transition season, during which the CTPEEs plays a pivotal role in plant growth and revival of terrestrial ecosystems. This study investigates the spatio-temporal variation characteristics of spring CTPEEs in China, including warm-dry, warm-wet, cold-dry and cold-wet combinations. The compound cold-wet extreme events occur most frequently, followed by warm-dry, warm-wet and cold-dry events. The frequency of CTPEEs associated with warm (cold) extremes shows a marked interdecadal increase (decrease) since the mid-to-late 1990s. It is found that the interdecadal change in CTPEEs is primarily determined by the variation in temperature extremes. This interdecadal shift coincides with the phase transitions of the Atlantic Multidecadal Oscillation (AMO) and the Interdecadal Pacific Oscillation (IPO). After the mid-to-late 1990s, the configuration of a positive AMO and a negative IPO excited atmospheric wave trains over mid-high latitudes, causing high-pressure and anticyclonic anomalies over East Asia. This leads to less cloudiness, allowing an increase in downward solar radiation, which enhances surface warming and contributes to an increase (decrease) in warm-dry and warm-wet extremes. The above observations are confirmed by the Pacemaker experiments. The results of this study highlight a significant contribution of internal climate variability to interdecadal changes in CTPEEs at the regional scale.
How to cite: Wang, L., Chen, S., Chen, W., Wu, R., and Wang, J.: Interdecadal Variation of Springtime Compound Temperature-Precipitation Extreme Events in China and its Association with Atlantic Multidecadal Oscillation and Interdecadal Pacific Oscillation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1776, https://doi.org/10.5194/egusphere-egu26-1776, 2026.
Compound events often exert more severe and widespread consequences than isolated extremes, and this effect is especially pronounced for those occurring at a regional scale. Here, we define and apply the concept of regional compound drought and heatwave events (RCDHEs) on daily scale to investigate spatiotemporally contiguous compound drought and heatwave events (CDHEs) across China during 1961–2022. We use homogenized observational data adapted from the China Meteorological Administration (CMA), and assess the performance of the state-of-the-art ERA5-Land reanalysis for its potential in supporting future studies. Rather than identifying events within fixed regions, we extract RCDHEs considering spatial and temporal coherence and characterize their dominant patterns through cluster analysis. The results reveal a marked increase in RCDHEs severity and duration across China over the recent decades. Over the mainland China RCDHEs can be grouped into eleven patterns. Most of these RCDHE patterns exhibit larger spatial extent, longer durations, and greater intensities during the recent decades. ERA5-Land effectively reproduces the various spatiotemporal features of these events; however, it consistently overestimates the frequency of RCDHEs across all region types since the late 1990s, limiting its reliability for assessing long-term trends. These findings enhance understanding of regional compound extremes in China and inform the appropriate application of ERA5-Land in future investigations of compound drought and heatwave events.
How to cite: Deng, J., Duan, Y., Ma, Z., Li, Z., Li, M., Wei, W., and Yang, Q.: Regional compound drought and heatwave events over China and evaluation of ERA5-Land dataset based on classification approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3230, https://doi.org/10.5194/egusphere-egu26-3230, 2026.
The Carpathian Basin is identified as one of the climate change hotspots in Europe. According to the latest data from the Copernicus Climate Change Service (C3S), the European continent – including Hungary – has already warmed by approximately 2.4 °C compared to the pre-industrial period (1850-1900), accompanied by more frequent extreme weather events. This substantial warming justifies the aim to focus on the detailed analysis of summer heat waves and droughts, especially their simultaneous occurrence. As demonstrated by the exceptionally hot and dry summer of 2022 in Hungary, the cumulative impact of these events poses severe consequences for agriculture, water management, and public health.
The main goal of our research is to explore the relationship between hot and dry periods in Hungary using homogenised, gridded daily maximum temperature and precipitation data from the HuClim database (0.1° spatial resolution) for the period 1971-2025. To investigate the spatial behaviour of the dependence strength between the monthly extremes of the base variables, a detailed cross-correlation analysis was completed. First, we analysed the spatial structure of monthly extreme temperature and precipitation fields separately using cross-correlation matrices based on different percentile values often used as extreme thresholds (i.e. the 75th, 90th, 95th, and 99th percentiles). In addition, we used anomaly maps to identify regions where extreme heat occurs with precipitation deficit at the same time. To investigate the duration of dry periods, we selected the Consecutive Dry Days (CDD) index calculated from daily precipitation data.
Our preliminary results indicate substantial differences in the spatial structure of the monthly variables. The analysis of the cross-correlation matrices demonstrates that while temperature fields follow a quite uniform, homogeneous pattern even in extremes, precipitation fields show a more heterogeneous structure. The joint evaluation of spatial anomalies (calculated as the difference between grid-point values and the regional mean) revealed substantial spatial heterogeneity. While mountainous regions show lower values due to orographic effects, the Great Hungarian Plain emerges as the most vulnerable 'hotspot' regarding the combined impact of heat waves and droughts, where the most pronounced positive temperature anomalies coincide with the greatest precipitation deficits. This is especially important due to the dominance of agriculture in the region, and suggests a clear necessity of adaptation strategies depending on further future climatic changes.
Acknowledgements. This work has been implemented by the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014) project within the framework of Hungary's National Recovery and Resilience Plan supported by the Recovery and Resilience Facility of the European Union.
How to cite: Fritz, P., Kis, A., and Pongrácz, R.: Analysis of hot and dry compound events in Hungary in 1971-2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8320, https://doi.org/10.5194/egusphere-egu26-8320, 2026.
Compound hazards represent a major challenge in hydrogeological risk analysis, as the co-occurrence of extreme conditions can generate complex and non-intuitive impacts, sometimes exceeding those produced by isolated extreme events. This preliminary study investigates the statistical relationship between droughts and landslides in Italy, with the aim to quantify the marginal and conditional probabilities associated with their co-occurrence and, thus, assess the relevance of drought–landslide compound events. The proposed analysis uses the historical series of Standardized Precipitation Evapotranspiration Index (SPEI), provided by the European Drought Observatory at multiple temporal scales, and the historical landslide occurrences provided by the ITALICA national catalogue. Specifically, for the analyzed period 1996-2021, a landslide–SPEI database is constructed by associating each grid cell in which at least one landslide occurred in the corresponding SPEI time series. As expected, a decrease in landslide frequency is observed during drought conditions. However such a frequency remains non-negligible, highlighting the need for multiple risk management strategies.
How to cite: Zofei, R. D., Palazzolo, N., Cancelliere, A., and Peres, D. J.: A statistical analysis of compound drought-landslide events in Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9599, https://doi.org/10.5194/egusphere-egu26-9599, 2026.
Compound dry and hot events (CDHEs), which arise from the co‑occurrence of heatwaves and droughts, represent one of the most critical and rapidly intensifying climate‑related hazards worldwide, particularly in climate change hotspots like the Mediterranean Europe. The consequences of these CDHEs often exceed those associated with isolated occurrences.
Despite growing recognition of their importance, CDHEs remain challenging to characterize due to their multivariate structure, requiring methodological approaches that differ from those typically employed in univariate analyses. As a result, advancing the study of CDHEs is essential, especially given expectations of their increasing frequency and severity under continued warming.
In this study, we employ a compound severity index based on the product of marginal probabilities of individually standardized hot and dry indicators, providing a meaningful measure of compound hot–dry severity across Europe. These indicators rely on well‑established metrics for defining heatwaves and drought conditions, including commonly used heatwave indices and the SPI, both derived from ERA5 data. The severity index is used to evaluate the spatial and temporal patterns of CDHEs for the period 1979–2025, with particular emphasis on distinct severity classes and the percentage of area affected by events.
The results show distinct spatial and temporal variations in CDHE severity and in the extent of the areas impacted. This perspective on joint magnitude and spatial extent allows for a consistent comparison of events, helping to identify those that were both exceptionally strong and unusually widespread across the domain, uncovering information that would be missed by analyses limited to event frequency.
Overall, this investigation advances the emerging field of compound‑event research by providing a detailed climatological assessment of heatwaves, droughts, and their co‑occurrence in a region already experiencing substantial climate pressures. The proposed framework offers a robust way to improve the representation of multivariate hazard characteristics and is expected to offer useful insights for climate‑impact assessment and risk management under continued warming. It further provides a solid starting point for expanding the analysis to include additional variables and processes linked to compound events, supporting more comprehensive evaluations of climate‑related risks.
Acknowledgements: 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, and DHEFEUS (https://doi.org/10.54499/2022.09185.PTDC).
How to cite: Santos, R., M. Gouveia, C., Bento, V., and Russo, A.: Spatiotemporal Patterns of Compound Heatwave–Drought Severity Across Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10559, https://doi.org/10.5194/egusphere-egu26-10559, 2026.
In early 2016, Kenya experienced a whiplash between two opposing extreme events: extreme heat in March followed by heavy rainfall in April. In particular, the North-East region (Mandera, Wajir, Isiolo, Marsabit, and Samburu counties) endured an ‘ultra extreme’ 20-day heat event, defined using the Heat-Wave Magnitude Index daily (HWMId), followed closely by a 4-day heavy rainfall period. This type of compound event, which involves a succession of individual events, is termed a ‘temporally-compounding event’ and can be particularly devastating as the initial event ‘preconditions’ the human and physical environment, thereby exacerbating the impacts of the second event.
There is a dearth of literature on compound events in East Africa, despite their increasingly common nature. Here we present an attribution methodology to disentangle the mechanisms driving temporally-compounding events to fill this gap. While attribution studies are still predominantly performed on individual extreme events, those which do consider compound events tend to focus on co-occurring multivariate events. The attribution of temporally-compounding events is, however, still in its infancy.
There are an additional range of factors to consider when attributing the drivers of a succession of hazards when compared to an individual extreme event. We build upon existing proposed methodologies to navigate these complicating factors, such as deciding between univariate or multivariate thresholds for event definitions, and deciding the ‘reasonable’ time interval between the cessation of the first event and the instigation of the second.
This research aims to contribute to the shared understanding of the interactions between the mechanisms driving compound events, specifically temporally-compounding events, within an East African context. This improved understanding can be used to inform locally-specific compound event definitions which can ultimately inform effective early-warning systems. By determining the relative contributions of anthropogenic climate change and natural variability on the 2016 Kenyan event, this research also hopes to lay the foundation for future attribution studies on compound events in the region.
How to cite: Eyles, H., Otto, F., Kimutai, J., Barnes, C., and Keeping, T.: The Attribution of Temporally Compounding Events: A Study on North-East Kenya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14496, https://doi.org/10.5194/egusphere-egu26-14496, 2026.
In a rapidly changing climate, various types of compound climate events (CCEs) have been widely analysed at both global and regional scales recently. Yet in the Baltic States, they have scarcely been studied. In this research three different CCEs were analysed: compound drought and heatwave events (CDHE), late spring frost events (FS events), and compound precipitation amount and wind speed extremes (CPWE). The aim of this study was to examine the recurrence, intensity, and spatial distribution of these CCEs from 1950 to 2022, and to assess projected changes in their characteristics by the end of the 21st century in the eastern part of the Baltic Sea region.
ERA5 reanalysis data were used to identify CDHEs, FS events, and CPWEs during the 1950–2022 period. Future projections were derived from five CMIP6 climate models using the NASA Earth Exchange Global Daily Downscaled Projections (NEX–GDDP–CMIP6) dataset under the SSP2–4.5 and SSP5–8.5 scenarios. Changes were assessed by comparing the period from 2081 to 2100 with a baseline period from 1995 to 2014. CDHEs were identified by calculating daily Standardised Precipitation Index (SPI) values to distinguish droughts and by defining heatwaves using the 90th percentile of daily maximum air temperature. CDHEs occurred when drought and heatwave conditions coincided. FS events were detected when the last spring frost occurred after the start of the growing season. Finally, CPWEs were defined as days when both precipitation and maximum wind speed exceeded their respective 98th percentiles at the same grid cell.
During 1950–2022, the number of CDHE days increased in over 75% of grid cells, mainly driven by a widespread rise in heatwave days (> 99% of grid cells). Also, FS events increased across more than 80% of the study area, while CPWEs became more frequent in 70.2% of grid cells. However, in most cases, the observed changes were small. They were statistically significant (p < 0.05) in less than 10% of the study area. Depending on the model and scenario, future projections indicate an increase in the number of days with CDHEs by the end of the century, with an average rise of 0.8–18.3 days/year. These events are also projected to become longer and more intense. CPWEs are expected to increase by 0.7–4.5 events/decade. Only the projections for FS events are uncertain, with different models indicating either increases or decreases in both frequency and intensity.
Distance from the Baltic Sea was found to have a strong influence on the spatial distribution of CCEs, with the highest number of CPWEs occurring in the western part of the study area. On the contrary, FS events and CDHEs occurred more frequently farther from the Baltic Sea coast. The results of this study suggest a potential increase in risks associated with CCEs in the Baltic States, underscoring the need for evaluations of climate adaptation strategies.
How to cite: Klimavičius, L. and Rimkus, E.: Spatiotemporal variability and future projections of compound climate events in the eastern part of the Baltic Sea region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17338, https://doi.org/10.5194/egusphere-egu26-17338, 2026.
The 1997 New Year's Flood was the most costly flood in California history, a compound extreme event driven by a category 5 atmospheric river carrying extreme precipitation, and amplified by snowmelt and elevated antecedent soil moisture. Previous work has successfully recreated the event using regionally-refined model meshes, identifying the major drivers of the flood, demonstrating the importance of model horizontal resolution in representing runoff totals, and demonstrating the warming sensitivity of these flood drivers. However, analyses have stopped short of estimating the likelihood of a comparable flood occurring under future climate conditions. Estimating the likelihood of single variable, much less compound extremes, under future climate is challenging, due to the multiplicity of uncertainty sources, the challenges in navigating deep uncertainties, and the limitations of comparable large ensembles and simulation capabilities between climate models. Building on recent work proposing a new conceptual methodology for "probabilistic storylines", the work presented here addresses this gap by estimating conditional probabilities for the 1997 flood storyline using the Energy Exascale Earth System Model (E3SM). Univariate and multivariate thresholds are calculated using the E3SM reanalysis, and return likelihoods and risk ratios under 1.5°C, 2°C, and 3°C global warming levels are estimated using the E3SM historical and SSP370 large ensemble. Multivariate joint probabilities of compound flood drivers are evaluated using copulas. Results quantify how the likelihood of a 1997 flood-like compound extreme evolves under different warming levels, conditional on the global climate sensitivity and regional structural uncertainty of E3SM.
How to cite: Longmate, J. and Rhoades, A.: Probabilistic Storylines: Conditional Likelihoods of the 1997 California New Year’s Flood Under Global Warming Levels, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18635, https://doi.org/10.5194/egusphere-egu26-18635, 2026.
Romania is a country situated in Southeast Europe, and due to its geographical position, it is exposed to different climatic hazards (heatwaves, floods, droughts). In the context of climate change, extreme weather phenomena are becoming increasingly frequent and intense, including in Romania. Compound events (CEs) are a combination of different hazards/climate drivers that can pose a significant risk to society and the environment. One type of CEs is multivariate events, when multiple hazards/climate drivers are co-occurring in the same geographical region. In this study, we analyzed multivariate events in Romania from 1940 to 2024 using CETD (Compound Events Toolbox and Dataset). This tool can generate the duration, frequency, and severity of compound events. Daily maximum temperature (tasmax), daily minimum temperature (tasmin), total precipitation (pr), mean surface wind speed (sfcWind), and mean wind speed at 500 hPa (preWind500) were extracted from ERA5 reanalysis dataset obtained from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS), with a spatial resolution of 0.25°x0.25°. We selected three hazard pairs related to extreme hot temperature: hot-dry, hot-stagnation, and hotday-hot night. Each hazard was defined as follows: hot (tasmax ≥95th percentile), dry (pr<5th percentile), windy (sfcWind ≥ 95th percentile), and stagnation (sfcWind<3.2 m/s and preWind500 < 13 m/s). The results indicate that specific areas in Romania are vulnerable to these three compound events, with a significant trend over recent decades, pointing to the need for effective risk mitigation implementation.
This study was carried out within Nucleu Program, contract number 24N/03.01.2023 (SOL4RISC), project no. PN23360202 and Catalina – Roxana Bratu’s PhD project at the Faculty of Physics, University of Bucharest. Contact: Drd. Catalina-Roxana BRATU, catalina.bratu@infp.ro. This work was supported by a grant of the Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-IV-P6-6.1-CoEx-2024-0042, within PNCDI IV.
How to cite: Bratu, C.-R., Antonescu, B.-A., and Ene, D.: Temporal evolution of extreme weather in Romania (1940-2024), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19037, https://doi.org/10.5194/egusphere-egu26-19037, 2026.
Although European agriculture faces escalating threats from climate extremes, current risk assessments focus on single hazards, overlooking compounding effects that drive devastating agricultural losses. This study presents a novel framework for assessing compound agroclimatic extremes in potato production across Europe, utilizing crop-specific physiological thresholds rather than generic meteorological definitions. We employed copula methods for extreme precipitation-temperature events and vine copula approaches for dry-cold-heat and wet-cold-heat compound events to characterize 32 compound extreme combinations across duration, intensity, magnitude, and frequency dimensions using ERA5 reanalysis data (1990-2024). Our analysis reveals striking spatial heterogeneity with pronounced north-south gradients. Mediterranean regions experience persistent hot-dry events lasting 3-4 days on average, but Northern Europe faces brief but intense cold-dry and hot-dry extremes. Key findings reveal nonlinear risk amplification under triple compound events, which exhibit intensity values 4-13 times higher than double events and magnitude anomalies 2-4 times greater. This amplification stems from synergistic interactions among temperature, precipitation, and land-atmosphere processes that generate cascading feedback exceeding individual hazard impacts. Cold-dry extremes emerge as the dominant threat, occurring 5-10 times more frequently than cold-wet combinations and at least 10 times more frequently than hot-wet extremes across central and northern Europe. Joint return period analysis reveals that severe hot-dry events occur every 1-3 years in Mediterranean hotspots, while moderate cold-dry events occur every 1-5 years across most of Europe. These results fundamentally challenge single-hazard agricultural risk frameworks and underscore the urgent need for adaptation strategies accounting for compound events. Our methodology, integrating crop-specific thresholds with comprehensive temporal characterization, provides a scalable and transferable approach for assessing agricultural climate risk across diverse cropping systems. The findings offer actionable insights for European potato cultivation planning and climate adaptation policies, highlighting critical hotspot regions where compound extremes pose the greatest threat to agricultural productivity and food security.
How to cite: Gohari, A., Saboori, M., Ghadimi, S., and Torabi Haghighi, A.: A mutivariate Framework for Assessing Compound Agroclimatic Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21668, https://doi.org/10.5194/egusphere-egu26-21668, 2026.
Learning Compound Climate Extremes: Generative AI for Hot–Dry Event Risk
In recent years, there has been a growing interest in the applications of machine learning methods to multi-hazard events, mainly due to their ability to ingest large amounts of data and capturing the relationships between variables. Compound weather and climate events (CEs) are of significant societal importance, as they present greater risks, and better understanding their response to climate change is crucial. This response has mostly been explored through dynamical climate model ensemble methods. However, accurately estimating the uncertainty of climate scenarios often requires very large ensemble simulations to be conducted, which can be computationally costly.
Generative deep learning methods offer a cost-effective alternative by enabling the generation of large sets of synthetic events which follow the joint distribution of high-dimensional data.
This work builds on the HazGAN framework, an ML framework which generates synthetic event sets for risk analysis of specific CEs in defined regions, capturing the dependence structure among variables. We utilise a HadGEM3 Large Ensemble to further condition the model on the large-scale climate background state, allowing the characteristics of the synthetic events to vary with different climate regimes. This approach aims to account for non-stationarity in compound event behaviour and to provide a physically consistent framework for exploring changes in hot–dry extremes under a changing climate. We also address uncertainties associated with using machine learning for extrapolation by rigorously testing out-of-distribution predictions. This work enhances the understanding of compound events, their risks, and future impacts under climate change scenarios
How to cite: Breitburd, T. and Colfescu, I.: Learning Compound Climate Extremes: Generative AI for Hot–Dry Event Risk, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22138, https://doi.org/10.5194/egusphere-egu26-22138, 2026.
Compound climate and weather extremes are increasingly recognized as key drivers of high-impact events, yet existing frameworks to assess their risk are often sector-specific and thus not broadly applicable. In this study, we develop an impact-based framework for characterizing and quantifying compound events and apply it to two case studies in the Netherlands. Our approach links multivariate meteorological conditions (the physical drivers) to sector-specific vulnerabilities, employs cut-offs based on stakeholder expertise, and draws on publicly available datasets (such as ERA5) to evaluate compound event risk. We apply the framework to two case studies: first, in the renewable-energy sector, we assess the occurrence of wind and solar daily "droughts" that lead to critical shortfalls in renewable power generation; second, in the agricultural sector, we analyze compound temperature–moisture constraints on crop primary productivity, quantifying the probability of extreme conditions detrimental to plant growth. Across both sectors, we employ empirical statistical methods to evaluate the frequency and co-occurrence of critical impacts, producing spatial estimates of return intervals for critical events, and assessing the tail-dependence of critical variables. We produce spatial representations of risk for both cases, which allow for the minimization of compound hazard potential in future planning where specific renewable energy mixes or crop types are assessed. For the energy sector, we identify the critical energy shortfalls as 2-, 3-, 4-, and 5-consecutive-day resource scarcity extremes and find their overall risk in terms of average return interval to be of 0.3-, 1-, 4-, and 5-year events based on 40 years of observations. The framework's reliance on identifying an impact of interest and characterizing key variables that control its associated risks enables its application across different sectors and regions, ideally supporting stakeholder engagement and decision-making.
How to cite: Fernandez Jimenez, A.: Impact-based analysis of compound hydroclimatic events in The Netherlands: case studies in energy and agriculture sectors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22701, https://doi.org/10.5194/egusphere-egu26-22701, 2026.
