The field of Detection and Attribution (D&A) identifies historical changes over long timescales, typically multi-decadal, of weather and climate as well as their impacts. D&A specifically quantifies the contributions of various external forcings as their signal emerges from internal climate variability. Driven by complex mechanisms, internal variability can itself change under external forcing, complicating D&A analyses and the projection of future changes. Moreover, event attribution (EA) assesses how human-induced climate change is modifying the frequency and/or intensity of extreme weather events (e.g. a heatwave), their impacts (e.g., economic loss or loss of life associated with flooding), or events from an impact perspective (e.g., a crop failure). These and other analyses focusing on attributing impacts combine observations with model-based evidence or process understanding. The attribution of climate change impacts is particularly complex due to the influence of additional non-climatic human influences.
This session highlights recent studies from the broad spectrum of attribution research that address some or all steps of the climate-impact chain from emissions to climate variables, to impacts in natural, managed, and human systems and aims to explore the diversity of methods employed across disciplines and schools of thought. It also covers a broad range of applications, case studies, current challenges of the field, and avenues for expanding the attribution research community. It specifically also includes studies that focus on the influence of specific externally forced changes as well as separating, quantifying, and understanding internal variability as both constitute a key uncertainty in climate attribution.
Presentations will cover common and new methodologies (improved statistical methods, statistical causality, Artificial Intelligence) using single climate realisations, large ensembles, or other methods to derive counterfactuals, on single climate variable or compound/cascading events, on impacts on natural, managed, or human systems.
Global mean sea levels have risen at an accelerating rate over the past century in response, primarily to greenhouse gas emissions from the combustion of fossil fuels. We use MAGICC7, a reduced complexity climate-carbon cycle model, to quantify how emissions traced to the Carbon Majors, the world’s 122 largest fossil fuel and cement producers, from 1854–2020 contributed to present-day surface air temperature rise, and sea level rise both historically and projected through 2300. We find that emissions traced to these industrial actors have contributed 37%–58% to present day surface air temperature rise and 24%–37% to the observed global mean sea level rise to date. Critically, these emissions through 2020 are expected to contribute an additional 0.26–0.55 m of global sea level rise through 2300. We find that attribution of past emissions to projected future sea level rise is robust regardless of how emissions trajectories evolve in the coming centuries.
How to cite: Sadai, S., Ranganathan, M., Nauels, A., Nicholls, Z., Merner, D., Dahl, K., Licker, R., and Ekwurz, B.: Estimating the sea level rise responsibility of industrial carbon producers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22711, https://doi.org/10.5194/egusphere-egu26-22711, 2026.
Extreme sea level (ESL) events, resulting from the combined effects of tides, storm surge, waves, and mean sea level rise, are a key driver of coastal flooding risk worldwide. While future increases in ESL magnitude and frequency due to anthropogenic climate change are well established, it remains unknown whether such changes, unlikely to be caused by internal variability alone, are already detectable in historical records, and whether their amplitude has emerged beyond the range of natural/internal variability at the global scale. Although a few studies have reported long-term increases in ESLs using historical tide-gauge records, the extent to which such changes are detectable and have emerged remains largely unknown, particularly at the global scale. This knowledge gap persists due to several challenges: (1) the infrequent, short-lived, and highly localized nature of ESL events, requiring high temporal and spatial resolution data; (2) the need for long, consistent time series to robustly characterize internal variability; and (3) the methodological complexity of detecting climate change signals and their emergence in extremes, compared to mean climate variables. Here, we assess the detection and emergence of climate change signals in ESLs along the global ice-free coastline using a 74-year (1950–2023) hydrodynamically modelled dataset with global coverage. Applying a detection and emergence framework tailored for extremes, we identify statistically significant increasing trends in ESL magnitude (global median 2.6 mm yr⁻¹) along approximately half of the global ice-free coastline. At nearly all computational points where a significant trend is detected, the associated ESL signal has already emerged beyond internal variability during the 1970–2023 period. The earliest times of emergence (regional-median ToE = between 1979 and 1982) occur in several IPCC reference regions, including the Equatorial Atlantic Ocean, Central Africa, the Equatorial Indian Ocean, Western Africa, Northeastern South America, Arabian Sea, and Northern South America. Linking times of emergence with national-level socio-economic indicators reveals that socioeconomically vulnerable countries with minimal historical CO₂ emissions have experienced increasing ESL magnitudes for the longest period.
How to cite: Nawarat, K., Tebaldi, C., Reyns, J., Muis, S., and Ranasinghe, R.: Global Patterns of Detection and Emergence in Extreme Sea Levels, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20355, https://doi.org/10.5194/egusphere-egu26-20355, 2026.
Water has long been a limiting resource in the world’s arid regions. In the western United States, an arid climate and demand from the region’s multibillion dollar agricultural industry have led to water shortages, legal disputes regarding water rights, and excessive overdraft of groundwater. Climate change is compounding these challenges by simultaneously reducing supply and increasing demand. Here, we combine both observational and modeling data to quantify how climate change and emissions from the largest 122 carbon producers have contributed to observed changes in regional water dynamics and explore the non-linearities underlying these relationships. Across the region, our findings show that climate change has reduced April snowpack by 44%, streamflow by roughly 16% and increased irrigation demand by 3.5%. Roughly half of these impacts are attributable to emissions traced to the Carbon Majors since 1950, translating to a nearly 18% reduction in snowpack, a 7% reduction in streamflow, and a 2% increase in irrigation demand. We also find a dramatic shift in the timing of warm season water availability, which occurs nearly two weeks earlier in some basins. Although this analysis focuses on the western United States, it reflects dynamics increasingly observed in arid and semi-arid agricultural regions worldwide, where climate change is intensifying competition over limited water resources. In total, our findings highlight the impact that emissions traced to only 122 corporations have had on water supply and demand across critical agricultural regions.
How to cite: Phillips, C., Williams, E., Abatzoglou, J., Safeeq, M., Sadai, S., Chegwidden, O., Mueller, N., Fernandez-Bou, A., Merner, L. D., and Ortiz-Partida, J. P.: Carbon Emissions Exacerbate the Western US Water Crisis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14655, https://doi.org/10.5194/egusphere-egu26-14655, 2026.
Certain high impact weather events are inherently multivariate. For instance, hot, moist, stagnant heat events are different than hot, dry, windy events. Not only are the impacts different; in this case human health vs. wildfire risk, the fundamental meteorology is also different. Generally, univariate indices have been constructed to be characterize impacts. However, nuances in changes in the multivariate nature of such events is lost with that practice. We present a methodology to calculate iso-surfaces of constant probability of rare combinations of meteorological variables. Key to the detection and attribution of changes in probabilities is quantifying the uncertainty in these iso-surfaces. If changes in iso-surfaces are found to be outside “confidence tubes” in the multivariate space, statistically significant changes can be said to be detected at a given confidence level. As an example, we consider the two dimensional case of temperature and relative humidity at various cities in the US and Mexico.
How to cite: Wehner, M., Butler, J., Castillo, F., and Sanchez, A.: Towards detection and attribution of multivariate phenomena, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8038, https://doi.org/10.5194/egusphere-egu26-8038, 2026.
How to cite: Thomas, R., Hawkins, E., Schurer, A., Thompson, V., Compo, G., George, S., Hegerl, G., Slivinski, L., and Shepherd, T.: Everyday weather in a warmer world, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13269, https://doi.org/10.5194/egusphere-egu26-13269, 2026.
The role of the stratosphere for decadal variability of surface climate is isolated using two sets of simulations in four different coupled ocean-atmosphere climate models. In the first set, the stratosphere (above 100hPa only) is nudged to observations (NUDGED) while allowing for the rest of the atmosphere to evolve freely, while in the second set the ocean-atmosphere system is free-running (FREE) and the stratospheric polar vortex does not exhibit any long term trends. By comparing NUDGED to FREE, we attribute to the stratosphere the anomalously cold conditions in the 2000s in high latitude Eurasia, and also the contemporaneous warm conditions in Eastern Canada. Furthermore, anomalously rainy conditions in much of Southern Europe in the 2000s can also be attributed to the stratosphere. This fingerprint from the stratosphere overwhelmed any forced signal from anthropogenic emissions.
How to cite: Garfinkel, C.: Attributing decadal variability in surface temperatures and precipitation to the Northern Hemisphere stratospheric polar vortex, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14042, https://doi.org/10.5194/egusphere-egu26-14042, 2026.
Despite high confidence in the global intensification of extreme precipitation under warming, substantial uncertainty remains in regional projections across climate models. Developing a process-based understanding of the physical drivers underlying this uncertainty is critical for improving future projections and informing adaptation strategies. Here, we apply a physics-based diagnostic framework to decompose projected changes in precipitation extremes and their uncertainty into thermodynamic and dynamic contributions. The thermodynamic contribution is relatively consistent across models and explains the globally mostly uniform intensification of extremes, whereas the dynamic contribution varies substantially among models and emerges as the dominant source of uncertainty, particularly in the tropics and midlatitudes. We find that the model uncertainty in the thermodynamic contribution cannot be simply explained by the local seasonal warming difference. Instead, there is a pronounced shift in the seasonal timing of precipitation extremes and thus the change in temperature on the day of extremes may substantially deviate from the seasonal mean warming, particularly across northern midlatitudes. We demonstrate that in many places to what extent the day of precipitation shifts into a cooler climate is the dominant uncertainty source of thermodynamic changes. Meanwhile, uncertainty in the dynamic contribution is primarily associated with inter-model differences in changes of updrafts. Notably, the change in updrafts at the 700 hPa level alone accounts for much of the model spread in precipitation extremes across the globe. These results highlight the key physical processes driving uncertainty in extreme precipitation projections and provide a foundation for targeted model evaluation and the development of observational constraints.
How to cite: Zhu, D., Pieper, P., Pfahl, S., and Fischer, E.: Understanding the model uncertainty of future changes in extreme precipitation events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17150, https://doi.org/10.5194/egusphere-egu26-17150, 2026.
Wildfires are dynamic components of the Earth system, responding to both natural variability and human activities. Although global burnt area (BA) has declined in recent decades, regional increases in fire severity have been observed, with significant impacts on ecosystems and human infrastructure. A wide range of observational data, including satellite-derived products, is available for monitoring and detecting wildfires and quantifying BA. While these datasets provide essential information on past and present fire activity, Earth system models allow a systematic investigation into the causal drivers of wildfire, and specifically its responses to anthropogenic forcing. However, the relative contributions of anthropogenic climate forcing and internal climate variability to wildfire activity often remain unclear, and those causal effects cannot be disentangled in observational datasets alone.
Using a storyline approach, we separate the thermodynamic and dynamic components of climate change, largely driven by human-induced forcing, and assess how these components affect BA. This allows attribution of wildfire responses to external forcing versus internal variability. Therefore, we analyze nudged circulation simulations from the Community Earth System Model Version 2 (CESM2; Danabasoglu et al., 2020) under different anthropogenic forcing scenarios. The pre-industrial simulation is based on a CO₂ concentration of 282 ppm, whereas the historical simulation uses time-varying historical CO₂ concentrations. Both simulations are nudged to horizontal winds from the ERA5 reanalysis, ensuring that large-scale circulation patterns are represented.
Here we present a first evaluation of wildfire characteristics in the CESM2 nudged simulations by comparing simulated output with observational data. Specifically, we compare mean values and trends in BA, fire season length, and the Fire Weather Index, as well as its individual components with GFED5 (Chen et al., 2023) for the period 2001–2020. To illustrate the potential of the framework, we present a case study of BA trends, highlighting how circulation-driven variability and thermodynamic changes can be separated across regions.
This evaluation provides a foundation for subsequent studies, using the nudged CESM2 simulations to represent key wildfire characteristics. The analysis forms a basis for attribution studies that disentangle the relative roles of changes in climate and land use in shaping differences between pre-industrial and historical scenarios.
How to cite: Eifler, L., Dunkl, I., Sippel, S., and Bastos, A.: From Observation to Attribution: Evaluating Nudged-Circulation CESM2 Burnt Area Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11842, https://doi.org/10.5194/egusphere-egu26-11842, 2026.
The intensification of the global water cycle under climate change is amplifying precipitation extremes, with disproportionate impacts on societies and ecosystems. Atmospheric rivers (ARs) are among the most important atmospheric drivers of extreme precipitation events, yet how climate change modifies AR-related precipitation remains poorly constrained, particularly at process level and for individual events.
Here we study the thermodynamic contribution to the intensification of AR precipitation by combining spectrally-nudged, kilometre-scale global storyline simulations (IFS-FESOM, 2017-2024) with a recently published state-of-the-arts AR catalogue (PIKART). The high spatial resolution of the simulations enables a physically interpretable attribution of precipitation changes to thermodynamic effects, while spectral nudging keeps large-scale circulation approximately fixed. This allows us to compare how AR precipitation unfolds under different levels of climate forcing. Precipitation responses are characterised using multiple complementary metrics, including total and extreme precipitation, precipitation rate, and spatial localizedness of precipitation.
We find an overall net intensification of AR-driven precipitation with almost global spatial extent (average intensification ~8%/K). The storyline framework further reveals the nature of precipitation changes at the event scale (where some events intensify by >20%/K), highlighting which ARs produce enhanced or reduced precipitation and through which mechanisms. To systematically assess these responses, ARs and precipitation are classified based on geometric properties and lifecycle characteristics (e.g. long- versus short-lived, oceanic versus inland-penetrating). I will present which constellations of AR/precipitation classes robustly produce more hazardous precipitation extremes.
This work represents one of the first global applications of a storyline approach focused explicitly on atmospheric rivers and provides a process-based perspective on how climate change modifies AR-driven precipitation extremes.
How to cite: Braun, T., Vallejo-Bernal, S. M., Mahecha, M. D., Sippel, S., and Dunkl, I.: Climate change modifies atmospheric river precipitation: a global storyline approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17246, https://doi.org/10.5194/egusphere-egu26-17246, 2026.
Since 2004, many methods for event attribution have been developed. Early studies showed that attribution statements are sensitive to the framing of research questions but few large comparisons have been undertaken.
Here, we firstly motivate the need for multi-method extreme event attribution, highlighting conceptual differences between methods. In a second part, we present a case study of midlatitude storm Babet (2023) to compare three common storyline attribution methods, alongside a severity-based probabilistic method. We discuss three widely relevant questions which highlight the complementarity and the differences between methods: (1) How has climate change impacted the frequency of the event? (2) How has climate change impacted the event severity? (3) Were the dynamics of the event influenced by climate change and if yes, how?
We show that methods differ in the extent to which they reproduce observed weather patterns. This influences attribution statements, and can even change the sign of results for events with uncertain climate signals. We argue that limitations and strengths of methods need to be clearly communicated when presenting event attribution reports to ensure findings can be used reliably by a wide range of stakeholders.
How to cite: Ermis, S., Thompson, V., Athanase, M., Zhou, L., Clarke, B., de Vries, H., Lenderink, G., Hope, P., Kew, S., Sparrow, S., Lott, F., Weisheimer, A., and Leach, N.: Multi-method extreme event attribution: Motivation, case study, and implications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4082, https://doi.org/10.5194/egusphere-egu26-4082, 2026.
Anthropogenic climate change (ACC) is one of the most demanding challenges facing our society. The intensification and increased frequency of many extreme events due to ACC are among its most impactful consequences, threatening human health, infrastructure, and ecosystems. In this context, raising awareness of the general public of the relationship between ACC, extremes, and associated impacts becomes a crucial task. To address this challenge, the ClimaMeter platform was developed [Faranda et al. 2024]. Its purpose is to provide rapid analyses of specific extreme events within hours of their occurrence, thereby contributing to public discourse and maximising media attention.
ClimaMeter is based on the analogue methodology for extreme events attribution [Yiou, 2014], which emphasises the dynamical processes associated with extremes by identifying weather situations similar to the event of interest, the so-called analogues. ClimaMeter leverages analogues to evaluate how events with the same dynamics as the one examined have evolved from 1950 to the present. Statistically significant changes between past and present analogues are assessed in terms of atmospheric circulation and associated meteorological hazards.
A key component of ClimaMeter’s methodology is the quantification of the relative influence of natural climate variability and ACC in explaining observed changes. Specifically, three modes of Sea Surface Temperature variability are taken into account: the El Niño-Southern Oscillation, the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation. These three modes are considered with equal weight, and changes not explained by them are, by default, assumed to be due to ACC [Faranda et al., 2024]. While the methodology is rapid and easy to communicate, it may have potential limitations.
In this work, we propose a new method, which we call ClimaMeter 2.0, that generalises and extends the original ClimaMeter approach. We propose two main modifications. First, the contributions of the three modes of variability are weighted according to the strength of their teleconnections with the event region and the specific hazard under consideration. Second, we explicitly test the assumption that climate change systematically influences the extreme under consideration. This generalised approach expands ClimaMeter’s methodology, increases its methodological flexibility, and provides new insights into the complex mechanisms linking natural variability and extremes.
Faranda, D., Messori, G., Coppola, E., Alberti, T., Vrac, M., Pons, F., Yiou, P., Saint Lu, M., Hisi, A. N. S., Brockmann, P., Dafis, S., Mengaldo, G., and Vautard, R.: ClimaMeter: contextualizing extreme weather in a changing climate, Weather Clim. Dynam., 5, 959–983, https://doi.org/10.5194/wcd-5-959-2024, 2024.
Yiou, P.: AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geosci. Model Dev., 7, 531–543, https://doi.org/10.5194/gmd-7-531-2014, 2014.
How to cite: Naldesi, C., Vrac, M., Faranda, D., and Bertrand, N.: An enhanced methodology to evaluate natural variability in ClimaMeter, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11860, https://doi.org/10.5194/egusphere-egu26-11860, 2026.
Event attribution has become an established field in climate science, with robust methodologies routinely used to assess the influence of anthropogenic climate change on different types of extreme weather events. At the same time, societal and scientific demand for timely attribution statements has increased, particularly in the immediate aftermath of extreme events. While rapid attribution approaches have made substantial progress and allow for the dissemination of attribution statements within weeks or even days, the overall workflow remains largely ad hoc and manual. In particular, event selection is often triggered by reported impacts or media coverage, which leads to uneven spatial coverage and systematic underrepresentation of events in regions with limited reporting, such as the Global South. Additionally, the manual nature of attribution analyses limits the number of events that can be assessed.
Among extreme weather events, heatwaves are particularly interesting for systematic attribution, as they often exhibit robust signals of anthropogenic climate change and are associated with substantial impacts on human health, ecosystems and socio-economic systems. Here we present an automated, hazard-based framework for the detection and attribution of heatwaves that enables continuous and systematic analysis with global coverage and flexible spatio-temporal scales. Heatwaves are identified directly from temperature data and attributed using established probabilistic methods. The framework is based on reanalysis products, forecast data and CMIP6 model simulations, allowing for both historical assessments and (near-)real time analyses.
The resulting inventory of heatwaves and associated attribution statements enables systematic comparison with existing attribution studies, including rapid attribution efforts such as World Weather Attribution [1], ClimaMeter [2] and Qasmi et al. (2025) [3]. Detected events are further compared with disaster databases, e.g., EM-DAT, to assess reporting biases and the consistency between hazard-based and impact-based heatwave records. Beyond comparisons of individual events, the resulting inventory of historical heatwaves also provides a basis for analysing regional changes in heatwave characteristics over time.
References:
[1] Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van Der Wiel, K., ... & van Aalst, M. (2020). A protocol for probabilistic extreme event attribution analyses. Advances in Statistical Climatology, Meteorology and Oceanography, 6(2), 177-203.
[2] Faranda, D., Messori, G., Coppola, E., Alberti, T., Vrac, M., Pons, F., ... & Vautard, R. (2024). ClimaMeter: contextualizing extreme weather in a changing climate. Weather and Climate Dynamics, 5(3), 959-983.
[3] Qasmi, S., Ribes, A., Cattiaux, J., Barbaux, O., Robin, Y., & Dulac, W. (2025). An automatic procedure for the attribution of extreme events at the global scale: a proof of concept for heatwaves. Bulletin of the American Meteorological Society, BAMS-D.
How to cite: Seeber, S., Schumacher, D. L., Quilcaille, Y., Gudmundsson, L., and Seneviratne, S. I.: A framework for the automated detection and attribution of heatwaves, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11925, https://doi.org/10.5194/egusphere-egu26-11925, 2026.
Extreme weather events are becoming more frequent and intense worldwide because of anthropogenic climate change, primarily driven by the increase in greenhouse gas concentrations. However, these changes are not spatially uniform: regional atmospheric dynamics, physiographic characteristics, and climatological regimes strongly influence the rates and patterns of change in extreme precipitation. In this context, the present study investigates the anthropogenic influence on the extreme rainfall event in São Sebastião, São Paulo (Brazil) on 18–19 February 2023. It was the largest rainfall event recorded in 24 hours in the modern history of Brazil, with more than 600 mm of rain in less than a day, according to rain gauges nearby. The event triggered widespread flooding, landslides, and debris flows, which led to severe socio-economic losses, including 65 fatalities and damages exceeding USD 120 million. Therefore, to better understand how anthropogenic influence has changed the magnitude and likelihood of this event, Extreme Event Attribution (EEA) provides a scientific framework for quantifying these changes. We use an innovative forecast-based attribution methodology using ensemble forecasts from the Integrated Forecasting System (IFS) of the ECMWF. Unlike traditional probabilistic attribution approaches—which evaluate changes in probability across broad classes of events—this method allows the analysis of the event itself, isolating the contribution of anthropogenic forcing while preserving the atmospheric dynamics that shaped the storm. This approach also incorporates a more traditional risk-based analysis using the HadGEM3-A climate model. The results show that the rainfall responsible for triggering the landslides was accurately predicted three days in advance by the ECMWF forecast system, demonstrating strong skill in anticipating the event’s magnitude and associated impacts. Considering the debris-flow and mudflow hazard threshold (>220 mm), both the Historical (40%) and Increase CO₂ (42%) scenarios show substantially higher exceedance probabilities compared to the Pre-Industrial (22%). When comparing the forecast-based and probabilistic approaches the largest absolute increase in precipitation occurs between Increase CO₂ (600 ppm) and Pre-Industrial (285 ppm), with approximately 57 mm (95% CI: 16.84, 97.09) of additional rainfall. The Historical – Pre-Industrial scenario results in an increase of about 46 mm (95% CI: 12.47, 79.10), while in the probabilistic approach, the anthropogenic forcing signal is estimated at approximately 50 mm (95% CI: 34.11, 65.87) and a narrower distribution of the bootstrap ensembles. Overall, the results demonstrate that human-induced climate change intensified the São Sebastião extreme rainfall event, increasing its magnitude and amplifying the associated impacts. The study also shows that results across different methods are similar, which suggests that the changes are consistent.
How to cite: Quintella Veiga, R., Cesario de Abreu, R., Pérez‐Fernández, I., Beray Armond, N., and Sparrow, S.: Attributing the 2023 São Sebastião Record Rainfall Using a Forecast-Based Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-825, https://doi.org/10.5194/egusphere-egu26-825, 2026.
Extreme rainfall events continue to cause severe flooding and landslides across regions with complex topography and string climate variability, particularly in the Global South. Using recent extreme rainfall and flooding events in Australia, Colombia, Venezuela, Mexico, Pakistan, Sri Lanka, Malaysia and Indonesia in 2025, we illustrate the roles of climate variability, human-induced climate change, d socio-economic and environmental drivers in shaping meteorological extremes and their impacts. These events are not meteorologically rare in today's climate, yet they produce disproportionate impacts due to high exposure, land use changes and social vulnerability.
Attribution analyses often reveal substantial disagreement among observational datasets, including satellite-based products, reanalyses and station records- in the magnitude, when highly localised heavy rainfall events are smoothed over in the gridded datasets, and in the direction of rainfall trends. Climate models also exhibit large inter-model spread and have limited skill in representing localised rainfall processes, complex topography and interactions with modes of climate variability such as the El Niño Southern Oscillation and the Indian Ocean Dipole. This is illustrated in the case of the extreme rainfall event in Mexico, where discrepancies among observational datasets, particularly prior to the satellite era due to sparse in-situ observations, combined with the influence of natural variability and mixed model trends, limit the ability to confidently detect or attribute long-term changes. In some instances, observational evidence indicates that short-duration rainfall extremes are becoming less likely, contradicting the physical expectation that a warmer atmosphere can hold more moisture. This apparent contradiction is explained by the timing of events within the seasonal cycle and the geographical extent over which trends are assessed. For example, the extreme rainfall event in New South Wales, Australia occurred in a region that represents both a geographical and climatic transition zone. Areas to the south exhibit a robust drying trend during the cooler months, while regions to the north show mixed rainfall trends. In addition, the event took place in May, a period of seasonal transition when shifts in atmospheric circulation become particularly important. Climate models struggle to represent these transitional regimes, contributing to discrepancies between observed and modelled trends in short-duration rainfall extremes. Consequently, while observational evidence suggests intensification of short-duration rainfall extremes in several regions, models often fail to reproduce these signals consistently, limiting confidence in quantitative attribution.
Overall, our findings show that uncertainty in extreme rainfall attribution is often shaped by limitations in observational coverage and model representation that challenges a straightforward attribution. This underscores the broader need to improve and maintain ground based observational networks, alongside the development of higher resolution models and improved representation of key physical processes in both operational forecasts and climate models. At the same time, robust assessment of climate change impacts requires integrating multiple lines of evidence, including physical understanding of atmospheric processes, regional meteorological context, and existing literature on rainfall trends, in order to properly contextualise results and avoid both overstating and understating the role of climate change in shaping extreme rainfall and associated risks.
How to cite: Zachariah, M., Barnes, C., Clarke, B., Keeping, T., Kimutai, J., and Otto, F.: When rainfall ‘misbehaves’: Drawing conclusions from attribution analyses for communities at risk., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13187, https://doi.org/10.5194/egusphere-egu26-13187, 2026.
2025 has been the third warmest year on the observational record globally, while the past decade has been the warmest on record. Accelerating climate change is altering the hydrological cycle, with associated extreme events - including droughts, floods, and compound multivariate extremes - occurring with increasing frequency and intensity in many regions of the world.
The 2025 October–November-December (OND), short rains (Vuli/Deyr/Hageya) season largely disappointed across East Africa, with major drought hotspots involving highly vulnerable regions in Somalia, eastern Kenya, and southern Ethiopia. Many areas received well under 60% of climatological precipitation level, with patches even below 30%, and some locations ranking among the driest in the satellite-era record (roughly since 1979). This substantial rainfall deficit, amplified by unusually high near-surface air temperature, rapidly degraded rangelands and diminished water availability, driving livestock stress and low crop yields. Additionally, in Somalia, the OND drought contributed to population displacements and heightened need for humanitarian aid.
We analyse the dynamic and thermodynamic drivers of the 2025 OND, Vuli/Deyr/Hageya drought in East Africa. Using a multi-method attribution framework, we assess the influence of anthropogenic climate change on the development of these high-impact drought conditions, with the strongest signal magnitude in the eastern Horn of Africa. We combine multiple observational and reanalysis datasets with large ensembles of bias-corrected CMIP6 historical simulations and future projections under SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. This enables us to evaluate the roles of climate-change indices and internal climate variability modes – most importantly ENSO and IOD - in shaping this extreme hydrological event on subseasonal to seasonal timescales.
Additionally, we use the Met Office HadGEM3-A attribution system to quantify the extent to which anthropogenic forcing has altered the probability and intensity of such class of meteorological drought in the region. We consider both unconditional and circulation-conditioned attribution perspectives. Preliminary results indicate a significant contribution of climate change to the likelihood and intensity of the 2025 Vuli/Deyr/Hageya drought over regions in Ethiopia, Kenya, and Somalia that would greatly benefit from adaptation measures.
How to cite: Fučkar, N., Mendes, J., Rowan, J., De Viveiros Teixeira, P. S., Allen, M., and Obersteiner, M.: Unconditional and Conditional Event Attribution of the 2025 Vuli/Deyr/Hageya, Short Rains (OND) Drought in East Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16656, https://doi.org/10.5194/egusphere-egu26-16656, 2026.
Anthropogenic climate change is known to intensify extreme precipitation events, which is generally assumed to follow approximately the Clausius-Clapeyron relation at 6% per K of warming. Whether the resulting flood hazards scale proportionally, however, remains uncertain. Here, we employ nudged climate model simulations to reproduce observed extreme precipitation events by constraining large-scale atmospheric circulation. We then conduct runoff simulations in which observed weather data are adjusted according to the nudged climate simulations, under both observed and pre-industrial greenhouse gas emissions. This approach allows us to isolate the thermodynamic contribution of climate change while avoiding uncertainties in potential large-scale circulation changes. We quantify both extreme precipitation and resulting flood hazards for more than 100 European flood events with observed impacts between 1981 and 2024.
Our simulations show that extreme precipitation intensity would have been on average only about 1% lower without climate change for floods that occurred before 2000, increasing to 5% in the most recent decade (2015-2024). Floods, however, intensify more substantially: climate change-driven precipitation changes would suggest a 7% reduction in peak flows without climate change. Yet higher temperatures have led to an increase in evaporation and thus drier antecedent soil conditions, offsetting some of this precipitation effect. Taken together, the net climate change impact on floods reduces to approximately 3% across events for the recent decade, which is no statistically significant climate change effect. These moderate changes on average mask a high variability across events, and sometimes even a high spatial heterogeneity within the same event; some floods would have been 20% less intense without anthropogenic climate change, with negligible mitigation effects from warming, while for others the flood hazard would have even been larger under pre-industrial conditions due to the combined climate change effects. This underscores the importance of conducting event-specific flood attribution studies to identify highly relevant changes at the local level.
Our results highlight that attributing precipitation extremes alone is not a good proxy for estimating climate change-induced changes of flood hazards. In particular, explicitly simulating floods and accounting for competing temperature effects provides a more complete picture. Reconciling these effects is crucial for public engagement with climate attribution results, and essential for future flood risk assessments and estimates of climate-change-related losses.
How to cite: Miersch, P., Dunkl, I., Sippel, S., and Zscheischler, J.: Beyond Attributing Precipitation Extremes: Warming Partly Counteracts Climate Change-Driven Flood Increases, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18669, https://doi.org/10.5194/egusphere-egu26-18669, 2026.
In November 2020, during the COVID-19 pandemic, two hurricanes hit Honduras in the span of two weeks. Hurricane Eta made landfall on November 4th, causing widespread damage across Central America, and particularly in Honduras. There was heavy precipitation, with widespread riverine flooding, landslides and flashfloods. The storm tragically caused at least 74 deaths. On 17 November, while the country was still responding to the immediate impacts of Eta, Hurricane Iota made landfall. Although the track was different, heavy precipitation affected the same area where Eta had already triggered floods and landslides – further exacerbating the impacts and causing 13 additional deaths. The storms had a direct reported impact on 437,000 people and indirectly affected 4.5 million, forcing the displacement of around 937,000 individuals as homes were damaged by flooding and landslides. Humanitarian needs surged, with 2.8 million people requiring assistance. The economic damage was severe, with losses reaching $1.9 billion USD and national economic growth declining by 0.8%.
To understand the potential role of climate change in exacerbating the flooding and the amount of people and buildings exposed to it, we simulate compound flooding (maximum water extent and depth) of Eta and Iota using a 2D hydrodynamic model (SFINCS) accounting for local precipitation, river discharges (calculated using the hydrological model wflow) and coastal water levels derived from global datasets. The model is used to simulate the factual and counterfactual scenarios. The factual simulation is based on present-day climate, whereas for the counterfactual scenarios, the precipitation and river discharges are adjusted to remove the long-term climate trend to represent pre-industrial conditions.
In addition, we also explore qualitative counterfactuals that demonstrate the compounding impacts resulting from the antecedent drought conditions, COVID-19 responses, and chronic insecurity and violence. This exploration emphasizes that the physical hazard and impacts are often strongly mediated and/or exacerbated by complex socio-economic and socio-political drivers and dynamics and highlight the role of non-climate drivers for severe impacts.
Overall, the results highlight the need for a holistic attribution perspective to develop effective response to reduce the impacts of future compound hurricanes.
How to cite: Vogel, M. M., Jack, C. D., de Boer, T., Aleksandrova, N., and Couasnon, A.: Attribution of compound flooding: The case of the subsequent hurricanes Eta and Iota in Honduras, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1592, https://doi.org/10.5194/egusphere-egu26-1592, 2026.
Event attribution studies increasingly rely on single–model counterfactual simulations (e.g. from the Detection and Attribution Model Intercomparison Project; DAMIP), or on statistical detrending to separate anthropogenic from natural variability (e.g. from the ATTRIbuting Climate Impacts framework; ATTRICI). However, there is currently no standardized observatio–constrained dataset that combines reanalysis with multi‑model forced trends from such existing intercomparison projects.
Here we propose, for the first time, to develop an ATTRICI–DAMIP dataset at a global scale, consisting of detrended reanalyses that approximate a counterfactual climate without anthropogenic forcing. Our main goals are to (i) derive anthropogenic ("hist–ant") trends from DAMIP historical (all forcings) and hist–nat (only natural forcings) experiments, (ii) apply these trends to remove the anthropogenic signal from multiple reanalysis products using the ATTRICI framework, and (iii) assess the suitability of the resulting fields for specific climate attribution applications.
We compute multi–model anthropogenic trends for key variables, including daily mean, maximum and minimum temperature, and precipitation, from DAMIP ensembles. Trends are estimated over 1950–2014, primarily using pattern scaling, and expressed on a common 0.5° spatial grid. Corresponding hist–ant trends are then subtracted from reanalysis fields to construct detrended pseudo–counterfactual time series. The resulting ATTRICI–DAMIP products are evaluated by comparing (a) the amplitude and phasing of variability against reanalyses, (b) trends and variability against hist–nat simulations, and (c) attribution metrics such as changes in distribution tails and Fraction of Attributable Risk for selected regional case studies.
We anticipate that the ATTRICI–DAMIP dataset should retain the realistic day–to–day variability and synoptic structures of reanalyses, while substantially reducing long–term anthropogenic trends in temperature and related variables. Our preliminary analyses using the ERA5 reanalysis and four process–based models (IPSL–CM6A–LR, MPI–ESM1–2–HR, GFDL–ESM4 and MRI–ESM2–0) indicate strong consistency between model–derived anthropogenic warming patterns and reanalysis trends over Europe. Here we expect to demonstrate how this new global–scale dataset can be used to quantify the anthropogenic contribution to recent high–impact events, particularly in under–studied regions such as West Africa and South-East Asia.
This work aims to provide a transparent, reproducible framework to merge DAMIP–based forced responses with reanalysis using the ATTRICI protocol, producing a new class of counterfactual datasets for climate attribution studies and supporting operational attribution and impact modelling.
How to cite: Brouillet, A., Menguel, M., and Undorf, S.: Constructing DAMIP-based detrended reanalyses for event attribution: design of the ATTRICI-DAMIP dataset, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11290, https://doi.org/10.5194/egusphere-egu26-11290, 2026.
Attribution research increasingly aims to quantify the full causal chain, from emissions to changes in climate extremes and their societal impacts. Furthermore, most extreme event attribution (EEA) studies remain focused on single events, and few assessments quantify contributions from specific emitting actors. Here we present a systematic framework that links historical greenhouse gas emissions to the changing likelihood and intensity of impactful heatwaves [1].
For all heatwaves reported in EM-DAT during 2000-2023, we apply an adaptation of the event attribution protocol by the World Weather Attribution using ERA5, BEST and CMIP6 data. After validation using goodness-of-fit and Granger causality, 213 heatwaves out of 226 are kept for analysis. Across events, we estimate how much anthropogenic warming since 1850-1900 altered the probability of occurrence and the intensity of these heatwaves. We then extend the attribution upstream to quantify the contribution of 180 carbon majors (fossil fuel and cement producers) to these changes, using company-level CO2 and CH4 emissions and reduced-complexity Earth system modelling (OSCAR) to derive counterfactual temperature trajectories and event-level heatwave metrics.
Results show that climate change made all 213 heatwaves more likely and more intense, with strong temporal escalation: the median heatwave in 2000-2009 became about 20 times more likely due to warming since 1850-1900, increasing to about 200 times in 2010-2019. About one-quarter of events were assessed as virtually impossible without climate change. Emissions from carbon majors account for roughly half of the increase in heatwave intensity since 1850-1900, and individual carbon majors’ contributions are sufficient to render 16 to 53 heatwaves virtually impossible in a pre-industrial climate into feasible events, depending on the actor.
By systematising EEA across many events, this work expands the scope of attribution for a more comprehensive perspective on heatwaves and enabling assessments across time and regions. Yet, we highlight limitations in the reporting of events, calling for more exhaustive datasets of events. By explicitly attributing portions of risk and intensity to major emitting entities, this analysis significantly contributes to the rapidly maturing field of source attribution, thus helping close an evidentiary gap between physical climate attribution and accountability-relevant quantification.
[1] Quilcaille, Y., Gudmundsson, L., Schumacher, D. L., Gasser, T., Heede, R., Heri, C., Lejeune, Q., Nath, S., Naveau, P., Thiery, W., Schleussner, C.-F., and Seneviratne, S. I.: Systematic attribution of heatwaves to the emissions of carbon majors, Nature, 645, 392-398, 10.1038/s41586-025-09450-9, 2025.
How to cite: Quilcaille, Y., Gudmundsson, L., Schumacher, D. L., Gasser, T., Heede, R., Heri, C., Lejeune, Q., Nath, S., Naveau, P., Thiery, W., Schleussner, C.-F., and Seneviratne, S. I.: Systematic Attribution of Heatwaves to the Emissions of Carbon Majors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13939, https://doi.org/10.5194/egusphere-egu26-13939, 2026.
Wildfires are among the most significant natural hazards, posing growing threats to ecosystems, human health, and infrastructure worldwide. In recent years, extreme wildfire events have occurred with increasing frequency across multiple regions, raising concerns that conditions once associated with exceptional fire seasons are becoming more common. The 2024–2025 fire year reflects this pattern, with particularly severe impacts in the Americas, including infrastructure and economic losses in western North America and major ecological, carbon, and air-quality impacts in the Amazon and Pantanal, alongside extremes in the Congo Basin. Understanding the role of human-induced climate change in driving these changes is essential for robust attribution and effective risk communication.
Here, the study presents an attribution analysis of individual extreme wildfire events that occurred between March 2024 and February 2025, as part of the State of Wildfires 2024-2025 report. Extreme fire-weather conditions were quantified using the Canadian Fire Weather Index (FWI; based on climate variables of temperature, precipitation, relative humidity, and wind speed), drawing on both observational datasets (ERA5) and the latest generation of global climate model ensembles (CMIP6). An established probabilistic event attribution framework is employed to evaluate the influence of anthropogenic climate change on the likelihood of high fire weather conditions, utilising simulations from the HadGEM3-A large ensemble to compare historical and present-day climates. A new empirical-statistical method was applied to project changes in risk under future global warming levels by using a set of CMIP6 models. Results indicate at least a threefold increase in the probability of extreme fire weather for three selected events from the past to the present period, with a further 1.5-6.4 times more likely to occur under future scenarios, underscoring the growing influence of climate change on wildfire hazards and the need for forward-looking fire management.
How to cite: Liu, Z., Kelley, D., Burton, C., Hartley, A., Ciavarella, A., Eden, J., and Parker, R.: Attribution of Extreme Fire Weather under Climate Change: Insights from the 2024–2025 Fire Seasons, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3124, https://doi.org/10.5194/egusphere-egu26-3124, 2026.
We perform a model-based end-to-end attribution of the July 2021 flood in the Ahr catchment to assess how anthropogenic climate change affected the likelihood of the event from extreme precipitation to direct residential building losses. The analysis compares a present-day (factual) and a pre-industrial (counterfactual) climate using an unconditional event attribution approach based on a Regional Flood Model (RFM) that links meteorological forcing, hydrological response, inundation characteristics, and impacts. Synthetic precipitation is generated with a non-stationary weather generator conditioned on large-scale circulation patterns and regional mean temperature and is used to drive the hydrological model mHM. Extreme flood hydrographs are routed with the hydrodynamic model RIM2D to derive flood depths and inundation extent, which are combined with exposure information in a flood loss model to estimate direct residential losses. Event likelihoods are derived consistently along the flood impact chain, and probability ratios between the factual and counterfactual climates are used to quantify climate change influence.
Results show that daily precipitation extremes comparable to the July 2021 event are about 1.2 times more likely in the current climate, while corresponding flood peaks are about 1.6 times more likely. The likelihood of inundation impacts increases more moderately, with probability ratios of 1.3 for average maximum inundation depth and 1.25 for inundation extent, and the likelihood of high residential building losses shows a probability ratio of 1.15. Overall, these synthesis results indicate that climate change has increased the likelihood of an event such as the July 2021 flood, with differing amplification factors reflecting the nonlinear transformation of climate-driven changes by hydrological, hydraulic, and damage processes acting on different spatial scales and response time-scales along the flood impact chain.
How to cite: Nguyen, V. D., Merz, B., Han, L., Apel, H., Guan, X., Kreibich, H., and Vorogushyn, S.: Attributing the July 2021 Ahr flood across the hydrological impact chain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7273, https://doi.org/10.5194/egusphere-egu26-7273, 2026.
In 2022, Europe experienced an exceptional and wide-spread compound heat-drought event, causing one of the most severe maize crop failures in recent decades. Understanding the influence of anthropogenic climate change on such high-impact events is essential for climate adaptation planning and agricultural risk management. Here, by combining observations, process-based crop model simulations from the ORCHIDEE-crop and climate model outputs, we show that maize yields across the European Union collapsed to 22% below trend expectations, with spatially compounding losses across multiple regions, particularly in eastern Europe. The 2022 crop failure was mainly driven by high temperatures during the reproductive phase. Anthropogenic climate change has already reshaped the climatic baseline for maize growth in Europe, with strongly increasing growing-season temperatures accompanied by weakly declining precipitation. These long-term changes have heightened the likelihood of concurrent heat and drought stress and increased the risk of crop failure events. Our factual and counterfactual simulations reveal that anthropogenic climate change has decreased EU maize yield by 25% (~17Mt production) in 2022, with reductions in top six producing-countries ranging from 15% to 40%. These results highlight that human-induced warming is now a key driver of European agricultural production risks, substantially amplifying the threats of compound heat-drought extremes to regional food security.
How to cite: Jian, Y. and Zscheischler, J.: Attributing the 2022 European maize failure on anthropogenic climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5201, https://doi.org/10.5194/egusphere-egu26-5201, 2026.
Child undernutrition is a major global concern, contributing to both mortality and lifelong morbidity. In low-income countries heavily reliant on subsistence and rainfed agriculture, low productivity agriculture affecting both food availability and income is one crucial factor influencing child nutrition. An increasing number of attribution studies have quantified impacts of human-induced climate change on crop yields in rainfed agriculture. Tracing the effect of these climate change impacts further to observed nutrition-related health outcomes is a gap in the attribution literature.
Here, we show an approach to attribute undernutrition-related child health impacts of anthropogenic climate change using a multidisciplinary array of data and methods. As a case study, we use India, which has one of the highest global burdens of undernutrition-related child health impacts. As observational data, we use 1) climate reanalysis data (W5E5), evaluated against observational products and weather station data; 2) district-level seasonal crop yield and production data for major staple crops; and 3) individual-level anthropometric and socioeconomic data from two waves of the Demographic and Health Surveys (DHS).
These data are used to bias-correct and statistically downscale large-ensemble climate model output from the CMIP6-DAMIP project, to calibrate perturbed-parameter ensembles of three different process-based crop models (APSIM, DSSAT, InfoCrop), and to estimate exposure-response functions linking crop yield anomalies to child health. Factual (with climate change) and counterfactual (without climate change) child health outcomes are then derived by applying this analysis chain to factual and counterfactual CMIP6-DAMIP data, respectively, and analysed in an event attribution framework to quantify the contribution of anthropogenic climate change to the 2014-2015 crop yield deficits and resulting child health impacts across India.
The 2014-2015 period saw substantial crop yield deficits across India, with seasonal yields falling more than two standard deviations below long-term trends. Epidemiological analysis reveals that children exposed to such deficits during prenatal and infancy periods face elevated stunting risk, while positive yield anomalies show no corresponding benefit. Attribution findings will be presented and wider implications for climate-food-health attribution and for applications of impact event attribution frameworks be discussed.
How to cite: Nübler, L., Abigaba, D., Barik, A., Bhaskar, P., Blum, T., Brouillet, A., DeVera, T., Gornott, C., Grimm-Pampe, N., Iyakaremye, V., Kouakou, E., Lejeune, Q., Tett, S., Waid, J. L., Wendt, A. S., and Undorf, S.: Event attribution of climate change impacts on child undernutrition via crop production in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18753, https://doi.org/10.5194/egusphere-egu26-18753, 2026.
New health attribution studies are emerging rapidly alongside advances in climate attribution methodologies. The increasingly complex research questions and the need for rapid estimates of climate change's contribution to recent observed events are driving the development of advanced frameworks in health impact attribution. These contributions combine advanced methods in climate epidemiology with the latest developments in climate trends and extreme weather event attribution to estimate climate-related health impacts attributed to anthropogenic climate change. However, integrating methods and data from these two fields is not straightforward, as methodological assumptions and limitations may not always align. One key element is the definition of the counterfactual scenario. Currently, counterfactual climates can be generated using a broad range of methodologies and under different assumptions, potentially leading to contradictory findings. This contribution aims to highlight the main methodological considerations when combining epidemiological data and methods with counterfactual climate data. Using data from the city of Zurich (Switzerland) as a testbed, we compare heat-mortality estimates across different sets of counterfactual scenarios generated by various methodologies. For example, counterfactual data that mimics day-to-day variation in the actual climate, suitable for assessing single events, would require vulnerability estimates and daily observed mortality at the time of the event. While GCM-based simulated counterfactuals (e.g., DAMIP), appropriate for measuring the anthropogenic signal in impacts over long periods, can be combined with average vulnerability estimates for the whole period or for specific subperiods to capture adaptation. This contribution will identify the key methodological elements that must be aligned and help guide the researchers in defining the study design and selecting the data for their impact attribution assessments.
How to cite: Vicedo Cabrera, A. M., Lüthi, S., Fischer, E., and Stuart-Smith, R.: On the use of different climate counterfactuals in health impact attribution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19213, https://doi.org/10.5194/egusphere-egu26-19213, 2026.
Dengue transmission is highly sensitive to climate variability, with increasing evidence that cascading and compound extreme events increase outbreak risk. In Brazil and Barbados, drought conditions at long temporal lags (4–6 months) followed by extreme wet conditions at short lags (1–3 months) have been associated with increased dengue risk, particularly when combined with elevated temperatures (Fletcher et al. 2025; Lowe et al. 2021). In Colombia, dengue incidence has similarly been linked to El Niño–Southern Oscillation (ENSO)–driven increases in temperature and reductions in precipitation (Muñoz et al. 2021), as well as to long-lag drought effects that vary across altitudinal and urbanization gradients (Kache et al. 2024; Lowe et al. 2021). Despite this growing body of evidence, the extent to which anthropogenic climate change has altered dengue risk through changes in extreme event patterns remains largely unquantified.
Here, we develop a predictive modelling framework that integrates the interacting effects of multiple climate extremes (drought, extreme rainfall, heatwaves) at short and long temporal lags to estimate dengue case incidence in Colombia under current climate conditions and under counterfactual pre-industrial climate conditions, thereby quantifying changes in dengue outbreak probability attributable to anthropogenic climate change. Multiple realizations of climate models simulating precipitation and temperature extremes under current and pre-industrial climate conditions will be used as inputs to the dengue risk model, allowing estimation of dengue outbreak probability attributable to climate change while explicitly characterizing uncertainty arising from both climate and epidemiological model components. This attribution framework will provide a transferable approach for quantifying climate-sensitive infectious disease outbreak risk attributable to anthropogenic climate change.
How to cite: Kawiecki, A. B., Moirano, G., Montenegro Torres, J. F., Luu, L., Li, S., Gebrechorkos, S., Vicedo-Cabrera, A. M., Stuart-Smith, R., Santos-Vega, M., and Lowe, R.: Attribution of Dengue Outbreak Risk to Climate Change-Driven Changes in Extreme Events in Colombia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21624, https://doi.org/10.5194/egusphere-egu26-21624, 2026.
Climate detection and attribution is essential for demonstrating the link between climate-related impacts, such as heat-related mortality, and human-induced heating. To date, limited studies have directly attributed the health impact of climate change to anthropogenic actions, and most of the existing studies are focused on regions in the Global North. Here we aim to quantify the heat-related mortality attributable to anthropogenic climate change in a low-income population in Brazil over the period 2000-2018.
We explore heat-related mortality in a low-income population in Brazil using the 100 million Brazilian cohort. This cohort accounts for approximately 60% of the total Brazilian population and contains individual-level information on people eligible for social-assistance programs. We first establish a heat-mortality relationship by generating relative risk curves using a Distributed Lag Non-linear Model (DLNM). We then use output from the Coupled Model Intercomparison Project phase 6 (CMIP6) Detection and Attribution (DAMIP) model ensemble to quantify heat-related mortality in factual and counterfactual climates.
We find that approximately 85,700 heat-related deaths can be directly attributed to human induced climate change over the period 2000-2018. This means that 4,500 heat-related deaths per year would not have occurred if human emissions of greenhouse gases had not warmed the climate. This amount of warming is estimated to be 0.92°C relative to a 1961-1990 baseline. We also estimate the attributable fraction of all-cause mortality in this low-income population in Brazil to be 2.45%.
Our findings demonstrate the urgent need to reduce greenhouse gas emissions and limit global heating to avoid future loss of life. They also highlight the ongoing inequality of climate change impacts and provide detailed understanding of the heat-related mortality burden faced by vulnerable populations in Brazil.
How to cite: Lieber, R., Andrade, R. F. S., Vosper, E., Cortes, T. R., Arrais, J., Souza dos Reis, J., Neves, D. J. D., dos Santos Ferreira, H., Haines, A., Jacobson, L. V., James, R., Lo, Y. T. E., Mitchell, D., and Barreto, M. L.: Heat-related mortality attributable to human-induced climate change in Brazil’s low-income population. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7004, https://doi.org/10.5194/egusphere-egu26-7004, 2026.
Climate change threatens education through various channels. These include extreme temperature effects on brain performance, extreme temperature and precipitation effects on agriculture affecting incomes, which in turn affects education funding and food security. Unlike other sectors where climate impacts are visible, educational losses remain poorly quantified and seldom attributed to anthropogenic climate change. This study provides the first and robust end-to-end attribution analysis of climate change impacts on educational outcomes in a developing country context, quantifying educational losses attributable to human-induced climate change and associated economic losses and damages in South Africa. We employ an integrated three-step methodological framework that integrates econometric modeling, climate emulation, and impact attribution science. First, we estimate causal climate-education relationships using panel fixed-effects regression with rich National Senior Certificate (NSC) examination data (2008-2023) merged with the Learner Unit Record Information and Tracking System (LURITS). We have acquired these datasets through collaborations with the Department of Education. Unlike previous studies using aggregated data, LURITS tracks individual learners from grade 9 to 12 across four complete cohorts (2017-2023), enabling precise allocation of cumulative climate exposure across all schools attended. This eliminates measurement errors from assumptions about school mobility. We specify temperature and precipitation as daily bins capturing extreme exposure within 15km buffers around each school, controlling for student-teacher ratios, school characteristics, and fixed effects. We will perform heterogeneity analysis across school quintiles and urban-rural locations. We will also explore whether agricultural channels amplify impacts through food security and income disruptions affecting school attendance and cognitive performance. Second, we generate counterfactual climate scenarios using IIASA's Rapid Impact Model Emulator (RIME). RIME interpolates global warming levels to produce grid-cell-level climate impact drivers under factual (with anthropogenic forcing) and counterfactual (without anthropogenic forcing) scenarios, requiring less computational power while maintaining methodological rigour for attribution analysis. This enables robust comparison of educational outcomes under observed versus counterfactual climate conditions. Third, we apply estimated coefficients from the impact model to both factual and counterfactual distributions of climate variables. The difference in predicted exam performance and dropout rates provides estimates of educational losses attributable to anthropogenic climate change. We extend attribution to specific historical emitter groups using RIME-X transformations following recent methodological advances in pollutant-source attribution. Economic valuation converts standard deviation losses into years of schooling lost and lifetime wage impacts using established education-earnings literature. Such papers include the World Bank paper that provided conversion estimates: one standard deviation loss in test score is equivalent to 5.75 years of schooling lost (Evans and Yung, 2019). This research will produce policy-relevant evidence for loss and damage discourse, adaptation prioritization, and climate justice frameworks. Our findings will inform efficient allocation of climate finance, provide evidence for climate-related litigation, and highlight intergenerational consequences of disrupted human capital formation in climate-vulnerable populations. This paper exemplifies interdisciplinary integration of econometrics and climate science to quantify anthropogenic contributions to socioeconomic losses, advancing both attribution methodologies and empirical evidence for global climate justice discourse.
How to cite: Mundowa, M. T., Winkler, H., Cilliers, J., Trisos, C., Simpson, N., and Taylor, S.: Educational Losses and Damages Attributable to Anthropogenic Climate Change: End-to-End Attribution Evidence from South Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9054, https://doi.org/10.5194/egusphere-egu26-9054, 2026.
Posters on site: Wed, 6 May, 10:45–12:30 | Hall X5
Extreme low sunshine duration (SSD) events exert strong constraints on surface energy balance, ecosystems, and solar power generation, yet the processes governing their occurrence and evolution remain poorly understood. From April 20th to May 4th 2024, Southeast China (SEC) experienced a record-breaking low SSD event, marked by an anomaly of -2.53 hours. Combining ground-based observations, reanalysis, and CMIP6 simulations, we show that this extreme event resulted from the joint effects of multiple interacting anomalous circulation patterns and anthropogenic forcing. A record-breaking anticyclone anomaly over the Bay of Bengal and the westward extension of the Western Pacific subtropical high established anomalous moisture transport into SEC, where it converged with cold-air intrusions steered by a Northeast Asia blocking high, producing a persistent frontal system. A spatially-weighted constructed flow analogues analysis attributes ~70% of the event’s severity to the atmospheric circulations, with the key patterns increasing the likelihood of the extreme event by a probability ratio (PR) of 1.46. Anthropogenic aerosols further increased the event probability (PR=1.97) through thermodynamic effects, whereas greenhouse gases (GHG) dominantly amplified the key circulation anomalies. Overall, the 2024 event arose from GHG-amplified circulation anomalies acting in concert with aerosol-driven thermodynamic effects. These results highlight a synergistic role of circulation–aerosol interactions in shaping extreme sunshine variability and provide a process-based framework for anticipating similar extremes in a warming climate.
How to cite: Zhang, M., He, Y., Dai, T., Zhou, J., Song, X., and Zhou, Y.: Anthropogenic and atmospheric circulation drivers of the record-breaking low sunshine event over Southeast China in 2024, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3652, https://doi.org/10.5194/egusphere-egu26-3652, 2026.
Identifying observed climate change signals in seasonal precipitation, as well as evaluating model representation of these trends, is challenging due to the significant influence of natural variability. In this work, we employ a dynamical adjustment methodology to isolate the contributions from large-scale atmospheric dynamics in the North Atlantic to winter precipitation in the UK (1901-2023) and a wider European domain (1950-2024). We then assess changes in total winter precipitation, as well as the contributions from the dynamical and non-dynamical components separately.
For the UK, we find a detectable and attributable change in non-dynamical precipitation, which has been scaling at 7.6 %/°C or approximately the Clausius-Clapeyron scaling rate. For the European domain, we find that a north-south spatial pattern emerges, with wetting trends in the mid-latitudes and drying trends in the subtropics. We show that dynamical adjustment methodologies greatly increase the detectable and attributable component of seasonal precipitation changes, which are significantly affected by large-scale dynamical variability.
How to cite: Carruthers, J., Fowler, H., Bannister, D., and Guerreiro, S.: Dynamical adjustment reveals detectable and attributable changes in European winter rainfall, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3896, https://doi.org/10.5194/egusphere-egu26-3896, 2026.
A central challenge in climate science is the assessment and quantification of anthropogenic temperature and precipitation change patterns in space and time, as well as the individual contributions from anthropogenic greenhouse gas (GHG) and aerosol (AER) forcings. While single model initial condition large ensembles allow robust separation of forced responses from internal variability and single-forcing simulations allow for the direct interrogation of individual drivers of climate change, understanding the interplay between AER and GHG responses remains difficult in observations, where only one realization of the climate system, subject to all forcings, is available.
Signal-to-Noise Maximizing Pattern Optimal Fingerprinting (SNMP-OF) offers a promising method to estimate the responses to AER and GHG forcing in a single climate realization. In a first step, Signal-to-Noise Maximizing Pattern (SNMP) analysis is used to characterize spatiotemporal anomaly patterns that are common across ensembles of single-forcing GHG or AER simulations and therefore likely represent an externally forced signal. Optimal Fingerprinting (OF) projects these patterns onto single ensemble members or observations to estimate the contribution of anthropogenic GHG and AER forcings to global temperature and precipitation changes. CNRM-CM6.1, CanESM5, HadGEM3, IPSL-CM6A, MIROC6 and CESM2 single- and all-forcings simulations of near-surface air temperature and total precipitation rate are used to test the method, by iteratively retrieving the forced response with SNMP from 5 of the 6 models and testing it with OF on single ensemble members of the remaining model. The skill in estimating the forced response in the left out model is compared to a benchmark method which is based on a simple scaling of the other 5 models. SNMP-OF is then used to estimate the AER and GHG forced responses in temperature and precipitation within observations.
Since GHG and AER exert partly opposing effects on the climate system, their separate quantification is essential for a physically consistent understanding of anthropogenic climate change and may provide more causal insight into observed climate trends.
How to cite: Stanimirović, I., Merrifield, A., and Jnglin Wills, R.: Testing An Optimal Fingerprinting Method to Separate the Greenhouse Gas and Aerosol Forced Responses in Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7268, https://doi.org/10.5194/egusphere-egu26-7268, 2026.
The summers of 2024 and 2025 have been the hottest and the third hottest in Serbia since 1951, respectively. Analysis of observational data and results of regional climate models classify the whole southeastern Europe region as especially vulnerable and threatened by climate change. In light of this, we attempt to answer the question: Have these extremely hot summers been intensified as a consequence of climate change? To answer this, we have followed the World Weather Attribution protocol and methodology (Philip et al., 2020), using data from ERA5-Land reanalysis for the whole region of Serbia and regressing the extreme summer temperature events on 4-year smoothed global mean surface temperature, which is considered to be the indicator for global warming. The variable chosen to describe the heatwaves is the maximum of the multi-day running mean of daily maximum temperature (TX) averaged over Serbia. For the shorter, but more intensive 2025 heatwave, running mean was calculated over 3 days, and for the substantially longer 2024 heatwave, a 10-day averaging period was selected. The results are presented in conjunction with the more traditional heatwave percentile-based analysis using explorative statistics. Synthesis of results obtained from reanalysis data and EURO-CORDEX multi-model ensemble has also been carried out to provide further confidence in the results.
Results for the 2025 event suggest the country-wide three-day average maximum temperature value of 35.3℃ is 11.75 times more probable in current climate conditions than those in the mid-20th century, while synthesis of reanalysis and model results suggests that this number is around 5. The same comparison was done for the extreme event of 2024 suggesting the country-wide ten-day average maximum temperature value of 33.8℃ is 11 times more probable in current climate conditions. The probability ratio increases with the increase of the averaging period, up to 19.39 for a 20-day moving window, with the synthesized results following with slightly lower values.
The scientific results were further translated into public reports and policy briefs and communicated to policy and decision-makers to support climate-risk awareness and adaptation planning.This study was supported and funded by the European Climate Fund (ECF).
References:
- Philip, S., Kew, S., Van Oldenborgh, G. J., Otto, F., Vautard, R., Van Der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and Van Aalst, M.: A protocol for probabilistic extreme event attribution analyses, Adv. Stat. Climatol. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, 2020.
How to cite: Tosic, M., Filipovic, L., Lazic, I., and Djurdjevic, V.: Climate change attribution of Serbian summer heatwaves in 2024 and 2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7833, https://doi.org/10.5194/egusphere-egu26-7833, 2026.
Attribution of regional climate change to anthropogenic forcing within the single realisation available from observations is an important but challenging goal for statistical methods in climate science. The correlation of regional conditions with global temperatures is a popular approach, especially in the context of attribution of impacts on downstream sectors such as health or economic outcomes. However, the influence of internal variability on this approach remains unquantified.
Here, we use large ensembles from three climate models as an idealised setting to quantify the role of internal variability for attribution of temperature and precipitation extremes. For temperature extremes, internal variability contributes uncertainties which exceed 50% of the climate change signal across at least 35, 25, and 5% of the global surface area in the MIROC6, MPI-ESM1-2-LR and CanESM5 models respectively. We demonstrate that a block-bootstrapping procedure applied to individual ensemble members can accurately capture the different levels and patterns of uncertainty observed within each large ensemble - opening the door to a robust application to the single realisation available in observations. For precipitation extremes, relative uncertainties are substantially larger - exceeding 100% of the climate change signal over 85, 70 and 50% of the global surface area. Moreover, applying block-bootstrapping to individual realisations does not accurately reproduce these uncertainties, indicating limits to this attribution approach for precipitation extremes at current levels of global warming. Spatial aggregation of precipitation extremes to scales of 5-10 degrees reduces uncertainties and improves the performance of the bootstrap, but does not do so entirely.
This work provides a basis for climate impact attribution from single climate realisations which can robustly capture the uncertainty driven by internal climate variability.
How to cite: Kotz, M. and Donat, M.: Capturing uncertainty from internal variability in climate attribution within single realisations , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13172, https://doi.org/10.5194/egusphere-egu26-13172, 2026.
The impact of increasing CO2 concentration under global warming includes altered surface energy balance, plant physiology and atmospheric moisture demand, which influences evaporative stress. We aim to attribute the impact of increasing CO2 concentration to the projected changes in reference evapotranspiration (ETo) under multiple climatic change scenarios over South Asia. We use second order Taylor series expansion to quantify the contribution of CO2 using modified penman monteith equation, where surface resistance is estimated as a function of CO2 concentration. The attribution framework enables assessment of scenario dependent CO2 impact on future aridity, water availability, water resource planning and agricultural adaptation.
How to cite: Saha, A. and Jain, M. K.: Assessing the contribution of elevated CO₂–induced surface resistance changes to referenceevapotranspiration trends over South Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15221, https://doi.org/10.5194/egusphere-egu26-15221, 2026.
In the sixth assessment report of the IPCC, working group 1 presented regional summaries of changes in hot extremes, heavy precipitation, and agricultural and ecological drought (Figure 3; Summary for Policymakers; IPCC, 2021). In particular, they summarized and classified the observed trends in these hazards to date and the confidence with which these trends could be attributed to anthropogenic forcings. These 'Hexagon maps' have become a useful tool for policymakers and scientists alike, providing a clear overview of current evidence and highlighting where further analysis might be prioritised. However, the number of trend detection and attribution studies, the science of extreme event attribution, as well as the strength of anthropogenic forcing, have all advanced rapidly in the intervening period since this report was published. For instance, according to a database of event attribution studies compiled by CarbonBrief, by late 2020 around 350 extreme event attribution studies had been published (CarbonBrief, 2024). By the end of 2024, an additional 270 studies were published, most of which used more advanced methods and datasets, and studied events occurring at higher warming levels.
Here, we present a framework for iterating these maps as new evidence emerges, with the twin aims of transparency and interpretability for the climate science community and beyond. Expert elicitation is necessary when compiling evidence across fields, analysis methods, experiment conditioning levels, hazard types, and temporal and spatial scales. We therefore seek to provide guidance as part of this formalised framework, both on the interpretation of these regional summaries and on the steps required to come to an overall conclusion that accurately reflects current evidence. To present this framework, we showcase regions in which the evidence base has grown since AR6 and where apparently conflicting lines of evidence may be reconciled through expert elicitation. It is hoped that this will lead to an iterative and open-sourced approach to the communities' knowledge of changes in extremes and their attribution to anthropogenic forcings.
How to cite: Clarke, B., Otto, F., and Harrington, L.: Emerging evidence of anthropogenic influence on weather extremes at the regional scale: Formalising and iterating the IPCC Hexagon maps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18252, https://doi.org/10.5194/egusphere-egu26-18252, 2026.
Probabilistic extreme-event attribution is based on a well-established methodology that quantifies the influence of anthropogenic climate change on weather-related extreme events. Most commonly, a generalized extreme value (GEV) distribution is fitted to observational, reanalysis, and/or climate model data with global mean surface temperature (GMST) as covariate. This results in a smooth evolution of the GEV parameters, with a prescribed linear GMST dependence of its mean parameter for temperature extremes and an exponential GMST dependence of its mean and spread parameters for precipitation extremes. In reality, however, the smooth (quasi-linear) GMST dependence can be broken by a complex interplay of how multiple forcings evolve and how extreme events respond. Introducing aerosol optical depth (AOD) as additional covariate in aerosol-affected regions has recently been shown to improve the representation of the historical evolution of heat-wave statistics, increasing confidence in expected future changes. Moreover, some event-based storyline simulations of extreme precipitation events exhibit an intensification that seems to level off at high (e.g., around +3°C) warming levels.
Here we use AWI-CM1 CMIP6 historical and scenario ensemble simulations to further explore such nonlinear dependences of heat and precipitation extremes in selected continental regions of the northern extratropics. Specifically, we compare GEV distributions fitted separately for distinct warming levels with those obtained from covariate-based fits. We find cases with pronounced nonlinearities that can be accounted for only partly by adding AOD as a second covariate. Our results underscore the need for careful interpretation of current probabilistic extreme-event attribution, particularly when extrapolating into the future, and highlight the importance of continued methodological development.
How to cite: Voelker, A. and Goessling, H.: Revisiting probabilistic extreme-event attribution under multiple forcings and nonlinear responses to global warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19199, https://doi.org/10.5194/egusphere-egu26-19199, 2026.
The increasing occurrence of extreme weather events since the beginning of the 21st century has led to the development of new methods to attribute extreme events to anthropogenic climate change. How the extreme event is defined has a major influence on the attribution result. A frequently disregarded or evaded aspect concerns the temporal dependence and the clustering of extremes. This study presents an approach for attributing complete time series during extreme events to anthropogenic forcing, which eliminates the need for declustering. The approach is based on a non-stationary Markov process using bivariate extreme value theory to model the temporal dependence of the time series. We calculate the likelihood ratio of an observational time series from ERA5 given the distributions as estimated from CMIP6 simulations with historical natural-only and natural and anthropogenic forcing scenarios. The spatial fields are condensed by the extremal pattern index as a compact description of spatial extremes. In addition, the study examines the extent to which attribution statements about the occurrence of extreme heat events change when the effect of the mean warming is eliminated.
The resulting attribution statement provides very strong evidence for the scenario with anthropogenic drivers over Europe, especially since the beginning of the 21st century. For central and southern Europe, the influence of anthropogenic greenhouse gas emissions on heatwaves could already have been proven in the 1970s using today's methods. Apart from a general rise in temperature, no other reliable signals could be detected, neither with regard to the temporal dependence of days with extreme heat nor with regard to the shape of the extreme value distribution.
How to cite: Friederichs, P., Meurer, P., Buschow, S., and Szemkus, S.: Non-stationary time series attribution for heatwaves over Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19298, https://doi.org/10.5194/egusphere-egu26-19298, 2026.
The need and interest in operational detection and attribution services is growing. To increase the operational efficiency of relevant and rapid attribution studies, an efficient method for the first step in the process - triggering and selection of extreme events for potential study, is required.
The “Heat wave magnitude index daily (HWMId)” of Russo et al. 2015 is adapted for the purpose of triggering heat, cold and wet extremes on a daily basis, where events are automatically ranked across a 2-week period according to an index accounting for their intensity, duration and area. The method applied to heat extremes will be presented here and results will be compared to HWMId for well known extreme events and other events of interest. As this is work in progress, we look forward to discussions on the challenges as well as the promising results of this method.
How to cite: Kew, S., Pinto, I., and Philip, S.: Automated trigger procedure for operational attribution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20950, https://doi.org/10.5194/egusphere-egu26-20950, 2026.
We present a method for quantifying the effect of climate change on local daily mean, maximum and minimum temperatures as well as their 2–31-day moving averages for locations that have long observational time-series available. The method utilizes CMIP6 (Coupled Model Intercomparison Project Phase 6) model data to estimate climate-change-induced shifts in both the mean and variance of local temperature distributions. As a case study, we apply the method to several weather stations in Fennoscandia for two 14-day periods in July of 2025, when an intense heatwave occurred in Northern Europe. We find that the station-specific average maximum temperatures in 12-25 July were 1.7 to 2.4 C higher, whereas the minimum temperatures in 19 July to 1 August were 1.5 to 2.4 C higher than they would have been in the beginning of the 20th century. Furthermore, these temperatures were made 3.4–9.8 times (maximum temperature) and 4.2–13.2 times (minimum temperature) more likely by climate change since the year 1900 that is assumed to represent pre-industrial conditions. The presented method enables operational meteorologists to assess the role of climate change in extreme temperatures in near real-time, for example for media reporting.
How to cite: Toropainen, A., Rantanen, M., and Räisänen, J.: A method for estimating the effect of climate change on mean, maximum and minimum temperatures on 1-31 day time-scales: Record-breaking heatwave of summer 2025 in Fennoscandia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16842, https://doi.org/10.5194/egusphere-egu26-16842, 2026.
We assess the relative roles of anthropogenic forcing and natural variability in recent climate change over the Tibetan Plateau using D&A frameworks. First, optimal fingerprinting applied to CMIP6 simulations and multiple observational datasets (stations and ERA5) quantifies seasonal changes in extreme precipitation indices (Rx1day and Rx5day) over 1961–2020. Anthropogenic signals are robustly detected in the increasing trends of spring and winter extremes, whereas natural forcing is not detected in these seasons. Second, we attribute the record-breaking 2022 compound hot–dry event using high-resolution statistically downscaled CMIP6 simulations and a Copula-based joint-probability framework to estimate return periods and risk ratios. Results indicate that human influence substantially amplifies the likelihood of extreme heat, drought, and their concurrence, and projections suggest a further increase in compound-event risk. Together, these lines of evidence show that anthropogenic forcing is a dominant driver of both long-term extreme changes and recent high-impact compound events over the Tibetan Plateau, providing actionable information for disaster risk management and adaptation planning in ecosystems, agriculture, and infrastructure.
How to cite: Dong, S., Li, W., and Xue, K.: Detection and Attribution of Climate Change over the Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8696, https://doi.org/10.5194/egusphere-egu26-8696, 2026.
Extreme Precipitation Events (EPEs) in Peru have major socio-environmental consequences, driving floods and landslides and causing serious damage to infrastructure and food security. To better identify and interpret these events despite the lack of a universal definition of “extreme” rainfall, the study proposes a data-driven framework that (1) fits probability distributions to monthly precipitation anomalies from the PISCO dataset (1981–2025) at each grid point to define local extreme thresholds, (2) measures event intensity with the Relative Exceedance Index (REI), and (3) uses K-means clustering to detect recurrent spatio-temporal regimes of extremes across the country. The results show that this approach successfully captures historically documented episodes of widespread impacts and reveals a small number of coherent, recurring spatial patterns of extreme rainfall. Composite analyses further indicate that these EPE regimes are systematically linked to large-scale climate anomalies: La Niña-related extremes tend to align with central Pacific cooling and North Atlantic warming that favor moisture inflow and heavy rainfall over the northern/central Andes, while El Niño-related extremes are tied to eastern Pacific warming that enhances onshore convection and moisture transport, intensifying coastal and western Amazon rainfall while South Atlantic warming further strengthening Amazon-focused extremes. Overall, this framework not only strengthens the detection and classification of extreme rainfall events, but also provides a robust approach to identifying large-scale oceanic sources of predictability that is crucial for anticipatory planning, risk management, and long-term adaptation strategies.
How to cite: Cuba Quispe, K. I., Khodri, M., and Chamorro, A.: Extreme Precipitation Events in Peru: A Data-Driven Classification and Large-Scale Ocean Controls over the past four decades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14634, https://doi.org/10.5194/egusphere-egu26-14634, 2026.
In late October 2024, the western Mediterranean experienced an extreme precipitation event centred over Valencia (southeastern Spain), producing record-breaking rainfall, flash floods, and severe societal impacts. The event was associated with a quasi-stationary cut-off low (COL; DANA in Spanish), which favoured sustained deep convection through strong instability, abundant moisture supply, and interaction with regional orography.
The COL organised an atmospheric-river-like moisture transport from northwestern Africa, while additional moisture was supplied by the anomalously warm Mediterranean Sea. To assess the role of anthropogenic climate change in amplifying this event, we apply a storyline-based event attribution framework using high-resolution (∼9 km) simulations from the European Union’s Destination Earth initiative. An ensemble of simulations is performed with the coupled IFS-FESOM model, spectrally nudged to ERA5 to constrain large-scale circulation, and compares two climate states: a Counterfactual (~1950) and a Factual (present-day) climate. This approach isolates thermodynamic effects while preserving the observed synoptic evolution.
Results show that the synoptic configuration alone was sufficient to generate extreme rainfall; however, human-induced warming substantially intensified the event. In the Factual scenario, atmospheric moisture content and horizontal moisture transport increased by 18–24%, convective instability (CAPE) increased by ~25%, and sea surface temperatures in the western Mediterranean were ~2°C warmer, enhancing evaporation. As a result, total precipitation over Valencia increased by ~20%, whereas peak precipitation rates on 29 October were ~36% higher, exceeding the Clausius-Clapeyron scaling implied by the mean warming across scenarios.
These findings, which agree with those obtained by independent researchers using alternative methods, demonstrate that anthropogenic warming significantly amplified the intensity of this Mediterranean extreme precipitation event through thermodynamic mechanisms, even without changes in large-scale circulation. High-resolution, physically consistent storyline simulations provide a robust framework for quantifying the contributions of climate change to individual high-impact events, thereby supporting impact-relevant attribution in vulnerable coastal regions.
How to cite: Campos, D., Grayson, K., Saurral, R., Beyer, S., John, A., Olmo, M., and Doblas-Reyes, F.: Using global spectrally nudged storylines to attribute anthropogenic amplification of the 2024 Valencia DANA extreme precipitation event, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12189, https://doi.org/10.5194/egusphere-egu26-12189, 2026.
Event attribution of single hazards has developed very rapidly over the last 20 years, with a wide range of methodologies now being commonly used. However, many devastating events occur as a result of more complex extreme hazards such as those of a compounding, cascading and sequential nature. In the COMPASS project the main goal is to produce a flexible and harmonised methodological framework for the impact attribution of compound extremes. This extends capability from both single-driver hazards to more complex extremes, and climate hazard attribution to the attribution of impacts. In this work we review the lessons learnt from a wide range of methods from the COMPASS Use Cases; including compound impacts from Storm Xynthia in France, Tropical Cyclones in East Africa, consecutive Hurricanes in Honduras and sequential storms and drought-heatwave impacts in the United Kingdom. The attribution approaches used in each Use Case vary significantly from storyline to probabilistic, covering a range of regions and event types. In this review, we summarise the key learnings from these Use Cases and make recommendations on the best methods for compound impact event attribution. The results emphasise the extra value of impact attribution, compared to attribution of the hazard alone, with significant non-linearities between changes in the hazard and societal impacts.
How to cite: Cotterill, D., Muis, S., Paprotny, D., Jack, C., and Terefenko, P.: Impact attribution methods for complex extremes; learnings from the COMPASS Use Cases , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3108, https://doi.org/10.5194/egusphere-egu26-3108, 2026.
The anthropogenic climate change causes some regions to warm faster than others. The poles experience a higher degree of warming than the equator but there are also regions showing different trends than their surroundings. One of these regions warming at an unexpected rate is Western Europe. The observed trends there, especially in single-day heat extremes but also in long, enduring heat waves are significantly higher than those projected in global (CMIP) or regional climate models (EURO-CORDEX) based on the 5th CMIP generation. The significant differences between models and reality were assumed to be caused by the fixed aerosol levels used in RCP-scenarios.
As the storyline approach developed further, this issue was also investigated, e.g. by Singh et al. (2023), and led to a different conclusion: Global climate models fail to represent the present atmospheric dynamic changes which has led to underestimated warming rates. Nevertheless, it remains unclear whether these dynamic changes are caused by anthropogenic effects or not.
The new generation of EURO-CORDEX based on CMIP6 is about to be published in 2026 (as of January 2026). The presented study investigates the differences between the regional model generations based on CMIP5 and CMIP6, especially in representing heat extremes in Western Europe. Although a possible explanation for the significant differences between climate models and recent observations was shown using storylines, it is still necessary to investigate how the differences in the model setups improve the statistical representation of heat extremes. In order to be able to rate the quality of the climate models for heat extremes, a comparison to gridded observational data like E-OBS is performed. This way, it can be seen which regions are well represented in regional climate models of one or both generations and in which regions the models are failing to capture the observed trends and therefore are unsuitable for standard probabilistic attribution studies.
The research of this project is part of the ClimXtreme Network, funded by the German Federal Ministry of Research, Technology and Space (BMFTR). Focus of this project are extreme weather events and impacts caused by anthropogenic climate change.
Literature: Singh, J., Sippel, S. & Fischer, E.M. Circulation dampened heat extremes intensification over the Midwest USA and amplified over Western Europe. Commun Earth Environ 4, 432 (2023). https://doi.org/10.1038/s43247-023-01096-7
How to cite: Schröter, J., Lorenz, P., Wagner-Jacht, M., and Kreienkamp, F.: Improvements in the representation of heat extremes in Western Europe in the new EURO-CORDEX generation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11834, https://doi.org/10.5194/egusphere-egu26-11834, 2026.
River flow is projected to change under global warming with impacts on both societies and ecosystems. In particular, shifts in the intensity and likelihood of extremely dry or wet conditions pose significant risks, including increased flooding, water scarcity, and disruptions to shipping, transportation, and aquatic habitats. However, while several extreme event attribution studies have investigated the role of climate change in the generation of selected hydrological extremes, a comprehensive and systematic assessment is lacking. One key hurdle for hydrological extreme event attribution is the limited availability of river flow simulations from climate model ensembles. The common practice is to deploy a case-study-specific modelling chain that relies on post-processed climate model output to drive a local hydrological model. While this ensures a high degree of adaptation to regional hydrological conditions, it implies a large methodological overhead and delays rapid assessment of hydrological extreme events.
To address this challenge, we investigate a novel approach that trades local precision for global coverage. We test if global climate model-driven river discharge simulations provide a suitable alternative when assessing changing probabilities of hydrological extremes in an extreme event attribution framework. Focusing on case studies to allow for the necessary attention to detail, we examine to what extent the probability of recent hydrological extremes has changed in both the observed record and an ensemble of climate model-driven river discharge simulations. To this end, we capitalize on a newly developed dataset of river discharge simulations derived by routing runoff from 18 CMIP6 and 25 ISIMIP3b simulations along the global river network. Both strengths and limitations of the proposed approach will be explored, especially given open questions on the role of anthropogenic climate change in recent disastrous hydrological extreme events worldwide.
How to cite: Seubert, P. B., Schumacher, D. L., Seneviratne, S. I., and Gudmundsson, L.: Attribution of extreme river flow conditions: A new framework using global climate model-driven river discharge simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14488, https://doi.org/10.5194/egusphere-egu26-14488, 2026.
Europe has experienced more frequent and severe heatwaves under continued warming, but the associated hazard has increased unevenly across cities. We ask what drives contrasts across cities and how these contrasts should be reflected in heat risk assessment, focusing on 36 major European cities.
We estimate Heatwave Intensity Duration Frequency (HIDF) using a block maxima approach. Specifically, for each city and duration, we extract annual maxima of heatwave intensity and fit HIDF separately for two multi-decadal periods (1950–1994 and 1995–2024). We then quantify the hazard shift and rank cities by the magnitude of change; in our current ranking, Milan, Paris, and Brussels exhibit the most significant increases, whereas Oslo, Stockholm, and Berlin show the smallest increases. To better understand why these contrasts emerge, we compute Heatwave Cumulative Intensity (HWC) and apply Time-Dependent Intrinsic Correlation (TDIC) to examine multi-scale, time-varying associations between HWC and candidate drivers.
Preliminary results indicate that several large-scale circulation indices (Arctic Oscillation, AO; North Atlantic Oscillation, NAO; East Atlantic pattern, EA; Scandinavian pattern, SCAND, etc.) exhibit broadly coherent associations across neighboring cities, even when hazard trajectories diverge. This pattern suggests that local conditions, such as soil moisture, dew point, and cloud cover, may play a significant role in modulating city-level hazard changes. Finally, following the IPCC AR5 framework, we integrate hazard derived from HIDF with exposure and a composite vulnerability index to produce risk-oriented mapping, highlighting areas where rising hazard coincides with high societal sensitivity.
How to cite: Chen, Y.-F. and Tsai, C. W.: Drivers of Uneven Urban Heatwave Hazard Across Europe: Mechanisms and Risk Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17616, https://doi.org/10.5194/egusphere-egu26-17616, 2026.
The pattern of Pacific sea-surface temperature (SST) change since 1980 has been highlighted as a key inconsistency between climate models and observations, with widespread impacts on the hydrological cycle, hurricane activity, sea level rise, and climate sensitivity. However, it is unknown whether this trend discrepancy results from discrepancies in the forced warming pattern simulated by climate models or discrepancies in simulated internal variability. Here, we use output of ForceSMIP, where statistical and machine learning models for distinguishing between forced response and internal variability within single realizations of the climate system were evaluated with climate model large ensembles and then applied to observations, to assess the forced and unforced contributions to Pacific SST trend discrepancies. We highlight a bias-variance tradeoff amongst the statistical and machine learning methods that show skill in forced response estimation, where methods that reduce the variance in estimated trends the most exhibit biases learned from the climate-model-based training data. Low-bias high-variance methods assess the trend discrepancy to be mostly forced, whereas low-variance high-bias methods assess the trend discrepancy to be mostly due to internal variability. The latter category of methods relies on training data from climate models with documented systematic biases, and we therefore suggest that more weight be put into the former category of methods, which would lead to the conclusion that the trend discrepancy is a discrepancy in the forced response. Our work illustrates the value of statistical attribution methods that are not reliant on climate models for interpreting trend discrepancies between climate models and observations.
How to cite: Jnglin Wills, R., Deser, C., McKinnon, K., Phillips, A., Po-Chedley, S., and Sippel, S.: Attribution of Pacific trend discrepancies using the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13653, https://doi.org/10.5194/egusphere-egu26-13653, 2026.
Internal modes of climate variability, such as El Niño and the North Atlantic Oscillation, can have a strong influence on distant weather patterns, effects that are referred to as “teleconnections”. The extent to which anthropogenic climate change has and will continue to affect these teleconnections, however, remains uncertain. Here, we employ a covariance fingerprinting approach to demonstrate that shifts in teleconnection patterns affecting monthly temperatures between the periods 1960–1990 and 1990–2020 are attributable to anthropogenic forcing. We further apply multilinear regression to assess the regional contributions and statistical significance of changes in five key climate modes: the El Niño–Southern Oscillation, North Atlantic Oscillation, Southern Annular Mode, Indian Ocean Dipole, and the Pacific Decadal Oscillation. In many regions, observed changes exceed what would be expected from natural variability alone, further implicating an anthropogenic influence.
How to cite: Vos, E., Huybers, P., Tziperman, E., and Goren, T.: Climate change alters teleconnections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2741, https://doi.org/10.5194/egusphere-egu26-2741, 2026.
