Yunlin County, as a major agricultural hub in Taiwan, faces critical challenges stemming from compound water stress under climate change. The interplay of rising demand, induced scarcity, and quality degradation exacerbates existing groundwater over-extraction and land subsidence problems. This research proposes an integrated framework to construct dynamic adaptation pathways that ensure physical and social robustness in water resource management.

The framework comprises three parts. First, we identify potential physical hazards associated with water resources in Yunlin County under climate change and analyze the causal interdependencies among different hazards. Simultaneously, we inventory all adaptation options and map these options to hazards, establishing a structure between risks and responses. Building upon this risk-response structure, the framework employs Dynamic Adaptation Policy Pathways (DAPP) to develop concrete adaptation pathways. The identified interdependencies are translated into tipping points and decision nodes within the DAPP framework, allowing for the construction of comprehensive storylines spanning from physical hazards to adaptive actions, and the strategy of policy making. Finally, to address social uncertainty inherent in policy implementation, the framework employs Agent-Based Modeling (ABM) for social stress-testing. By simulating stakeholder decision-making, ABM reveals how agent interactions influence the environment. We refine the pathways based on ABM outcomes, integrating social perspectives into the storylines. Furthermore, we incorporate water balance, agricultural income, and land subsidence into the evaluation, utilizing Multi-Criteria Decision Analysis (MCDA) to develop a dynamic adaptive plan.

By establishing this integrated system, this research aims to utilize DAPP and ABM to formulate robust adaptation strategies. It provides policymakers with a broader vision of the complex trade-offs between water scarcity, social feasibility, and agricultural systems.

How to cite: Lin, S.-E.: Socio-Hydrological Storylines under Deep Uncertainty: Applying DAPP and ABM to Compound Water Stress and Subsidence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4009, https://doi.org/10.5194/egusphere-egu26-4009, 2026.

During the winter of 2023/24, northern France experienced two consecutive flood events that caused severe losses and damages in the region. Although the region is well-known for being exposed to flooding, the impact of these events was much greater than that of previous floods. Hundreds of municipalities were declared damaged while hundreds of houses were flooded. Some places and people were flooded twice during the winter. 

This work aims to understand the physical and societal conditions that led to these impacts. We conduct an event-based storyline to investigate the flood hazard, the exposure of the inhabitants of the territory and their vulnerability (Sillmann et al 2020). The approach allows us to denaturalise disaster (Klinenberg, 1999) by studying the links between hazard and impacts, but also between exposure, vulnerability and impacts. This is done by combining various datasets. 

The hazard analysis is based on long-term meteorological and hydrological observations. This enables us to identify the hydro-climatic drivers of the flood. We show it is the combination of the succession of eight storms and almost continuous rain during winter 2023/24 that led to extreme rainfall accumulation. The study of winter weather regimes based on ERA5 data explains the persistence of those drivers. Using Mann-Kendall statistics, we demonstrate that the hydro-climatic drivers observed during the flood events fall within a long-term trend towards higher average and extreme precipitation in Nord-Pas-de-Calais. We investigate the compound nature of the 2023/24 flood events (Zscheischler et al 2020). The succession of eight heavy precipitation events leading to two flood events emphasises the temporarily compound nature of the events. In addition, we explore the multi-variate compoundness of the event, through observations of the high tidal coefficients, the land use and land coverage during winter 2023/24, which can all be partly responsible for the flooding.

Finally, we use past flood events as milestones to compare to 2023/24 flood events, to better understand the drivers, both meteorological and non meteorological, which led to such extreme flooding.

Klinenberg, Eric. s. d. Denaturalizing Disaster: A Social Autopsy of the 1995 Chicago Heat Wave.

Sillmann, Jana, Theodore G. Shepherd, Bart van den Hurk, et al. 2021. « Event-Based Storylines to Address Climate Risk ». Earth’s Future 9 (2): e2020EF001783. https://doi.org/10.1029/2020EF001783.

Zscheischler, Jakob, Olivia Martius, Seth Westra, et al. 2020. « A Typology of Compound Weather and Climate Events ». Nature Reviews Earth & Environment 1 (7): 333‑47. https://doi.org/10.1038/s43017-020-0060-z.

How to cite: Doury, E., Jézéquel, A., Habets, F., and Fildier, B.: Storyline of the winter 2023/2024 flood events in Nord-Pas-de-Calais (France), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7517, https://doi.org/10.5194/egusphere-egu26-7517, 2026.

How to cite: Gomez-Zapata, J. C., Siebert, A., Martyr, R., Guerra, M., and Schaeffer, M.: Storyline-Based Modelling of Cascading Critical Infrastructure Impacts and Recovery in Small Island Developing States, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15223, https://doi.org/10.5194/egusphere-egu26-15223, 2026.

Socioeconomic and demographic factors such as age structure, gender distribution, and education levels play a key role in shaping social vulnerability to climate-related risks. The Shared Socioeconomic Pathways (SSPs) provide national-level projections of these variables under different future scenarios, but these aggregated estimates neglect the spatial heterogeneity that drives local vulnerabilities. This study introduces a novel methodology for downscaling national SSP projections to subnational administrative units (NUTS2) in Europe. The methodology is illustrated for the SSP3.1 scenario and includes, first, the calculation of region-to-country ratios, analysis of historical trends, and validation of the model by quantifying its agreement with observed historical time series. National projections are then either downscaled to the administrative unit level and adjusted for temporal trends where they are statistically significant, or downscaled using 2020 reference proportions. The resulting dataset provides spatially explicit, SSP3.1-consistent projections that capture subnational variability while aligning with national trends. This dataset could support a wide range of applications, including climate impact assessments, socioeconomic modeling, and adaptation planning. By prioritizing transparency and replicability, this study offers a valuable resource for researchers and decision-makers seeking subnational socio-demographic projections for Europe.

How to cite: Sestito, B., Reimann, L., Bonatz, H., Botzen, W., Aerts, J., and Mazzoleni, M.: Downscaled Population Projections Under Shared Socioeconomic Pathways: A European Wide Application for Age, Gender and Education, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7535, https://doi.org/10.5194/egusphere-egu26-7535, 2026.

We introduce a novel approach to event attribution and developing storylines based on both recent and historical observed extreme events. Using the 20th Century Reanalysis system (20CRv3) we produce factual global reconstructions of observed events from different periods - the examples shown here are for a range of event types from 1910, 1976 and the last decade.

For modern events we produce a cooler counter-factual by reducing the SSTs used as boundary conditions and greenhouse gas levels in the reanalysis and assimilate the same surface pressure observations to produce the ‘same’ weather patterns in a cooler world. For the historical examples we produce a warmer counter-factual by increasing the SSTs and greenhouse gas levels to represent the same weather in a modern climate. The differences between factual and counter-factual provide estimates of the change in intensity of the observed event as represented by a modern numerical weather prediction model.

This approach allows a global perspective on extreme events and their impacts - the same experiments produce global factual and counter-factual reconstructions of every day in the chosen periods. The data will be made openly available to allow anyone to explore their own choice of extreme event anywhere in the globe. Counter-factuals will also be developed for future warmer climate conditions to understand how extreme events and their impacts will change, and help inform adaptation decisions.

How to cite: Hawkins, E., Thomas, R., Thompson, V., Schurer, A., Shepherd, T., Hegerl, G., Compo, G., Slivinski, L., and George, S.: Reanalysis-Based Attribution and Storylines of Extremes (ReBASE), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9957, https://doi.org/10.5194/egusphere-egu26-9957, 2026.

Climate change–driven extremes, including intensified rainfall and heatwaves, increasingly threaten urban systems not through isolated hazards but through cascading failures embedded in infrastructure interdependencies. In urban areas, outdated drainage systems may exacerbate flooding impacts by constraining electricity access and recovery during flooding, whereas concurrent power outages may further impair the pumping capacity, monitoring, and operational control of drainage systems. These coupled dynamics often result in nonlinear, system-wide functional collapse without identifying the respective system’s criticality in their operative conditions. Yet studies have been focused on evaluating water and energy system vulnerability independently and relying on analysis based averaged damage metrics, rendering them structurally incapable of capturing abrupt transitions and amplification processes arising from infrastructural interdependency.

This study develops a scenario-based analytical framework to examine how interdependent urban water–energy systems respond to climate extremes and under what conditions their dynamic behavior undergoes regime shifts. Water and energy infrastructures (i.e., drainage and sewer systems, and power grid systems) are conceptualized as integrated Social–Ecological–Technological Systems (SETs), allowing social capacity, ecological buffering, and technological performance to be analyzed within a unified system structure. Based on this theoretical framework, a Causal Loop Diagram (CLD) is constructed to explicitly represent feedback mechanisms and cascading failure pathways linking drainage capacity, power reliability, and damage recovery dynamics.

Building on the conceptual model, a System Dynamics (SD) approach is employed to explore coupled system behavior across scenarios that vary climate shock intensity, infrastructure functional degradation, interdependency-driven amplification, and the timing of policy intervention. Central to the analysis is the identification of critical transitions through a threshold-state variable that captures shifts from adaptive system functioning to persistent systemic stress. Rather than assuming proportional responses, the model identifies combinations of climatic and infrastructural conditions under which marginal perturbations produce self-reinforcing and potentially irreversible system responses. Results from the scenario analysis indicate that proactive interventions implemented prior to threshold crossings are substantially more effective in suppressing cascading dynamics than reactive measures introduced after system destabilization.

This study aims to advance urban climate adaptation research by reframing infrastructure resilience as a problem of system transition under interdependency, rather than isolated performance failure. By integrating threshold identification analysis, interdependent infrastructure dynamics, and scenario-driven simulation, the proposed framework offers a transferable foundation for designing anticipatory adaptation strategies capable of preventing regime shifts in urban systems under climate extremes.

How to cite: Gayoung, L. and Yeowon, K.: Scenario-Based Identification of Critical Thresholds in Interdependent Urban Water–Energy Systems under Climate Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6337, https://doi.org/10.5194/egusphere-egu26-6337, 2026.

While in many regions worldwide climate change and socio-economic developments are increasing the likelihood of unprecedented extreme events, current risk management practices are often not prepared for such events, resulting in catastrophic impacts. This lack of preparedness is partly driven by the reluctance of both lay people and decision-makers to consider and plan for events that exceed those observed in the historical record. There is thus a need for approaches that generate plausible scenarios of unprecedented events that are both scientifically sound and intuitively understandable. Here we present several methods for constructing such scenarios for river flooding in Germany. These include spatial counterfactuals, in which the precipitation fields of historical floods are spatially shifted, and a perfect-storm approach, in which precipitation from historical events is combined with historical wet catchment conditions. In addition, we apply a stochastic simulation framework in which a large-scale weather generator drives a hydrological model. All three approaches produce events that are substantially more severe than those observed in Germany over the last 70 years (1951-2021). For example, even moderate deviations in the trajectory of the precipitation field of past floods, which were among the most expensive and catastrophic events in Germany, could have led to substantially higher severity across Germany. While all methods are able to provide unprecedented flood events, the choice of method depends on the intended application, such as stress-testing infrastructure or supporting risk communication.

How to cite: Merz, B., Nguyen, V. D., Han, L., and Vorogushyn, S.: Exploring Unprecedented Flood Events Using Counterfactual and Stochastic Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7336, https://doi.org/10.5194/egusphere-egu26-7336, 2026.

How to cite: de Melo Viríssimo, F., Dookie, D. S., Hunt, A., Athanassiadou, M., Dawson, M., Beswick, A., Gannon, K., Ellis, M., Harrington-Abrams, R., Love, A., Mehryar, S., Piccaro, E., Rusby, C., and Thornton, A.: Piloting climate storylines in adaptation finance as a tool to shift the political economy of adaptation policy and bankability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7451, https://doi.org/10.5194/egusphere-egu26-7451, 2026.

The Danube River Basin is a vital artery for European energy production, food security, and inland transport, yet it is increasingly emerging as a hotspot for hydroclimatic extremes, particularly droughts. Although global thermodynamic warming signals are robust, regional climate change projections remain uncertain due to the large impact of atmospheric circulation at these scales. In particular, the seasonal response of the North Atlantic jet stream to forcing is not robust across models. Here, we define physical climate storylines based on CMIP6 data to partition the uncertainty associated with diverging jet stream responses in speed and latitude. Subsequently, we use bias-adjusted CMIP6 projections to generate hydrological simulations for the Upper Danube Basin, focusing on the high-emission scenario SSP5-8.5 but finding similar results for SSP2-4.5. We identify an intensification of historically rare low-flow events in several storylines at a +2°C and +3°C global warming level. Notably, return periods in winter are modulated depending on the jet stream response. Consequently, adaptation planning must move beyond historical benchmarks to prepare for a reality of more frequent water scarcity in the future.

How to cite: Weis, V. L., Stanzel, P., Kling, H., and Ossó, A.: Change of return periods for low-flow extremes across storylines in a warming Danube River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10258, https://doi.org/10.5194/egusphere-egu26-10258, 2026.

Understanding historical and future patterns of urbanization is essential for anticipating demographic change, guiding sustainable development, and managing climate and hazard risks. Although Shared Socioeconomic Pathways (SSPs) incorporate urbanization conceptually, few settlement projections have been adapted to fine spatial scales that capture intra-urban heterogeneity. Because most growth in developing-country cities occurs via horizontal expansion at the peri-urban fringe, improving spatially explicit models of land conversion and build-up dynamics remains a key methodological need.

We evaluate alternative open and reproducible modeling approaches for the Accra metropolitan area (Ghana), a rapidly growing and spatially uneven urban region. Using satellite-derived land use/land cover and built-up layers (2001, 2005, 2009), we compare (i) established urban growth modeling (UGM) toolchains focused on binary expansion (MOLUSCE, FUTURES, SLEUTH) against a flexible statistical learning baseline (LEARN), and (ii) newer approaches that model the continuous built-up surface directly. For the expansion-focused setup, models are trained on 2001–2005 and evaluated by predicting 2009 transitions using a shared covariate set (e.g., prior urban extent/LULC, distance to roads and waterways, protected areas, elevation, and distance to existing development).

Performance is assessed using the Figure of Merit (FoM), a change-focused accuracy measure that avoids inflated scores under rare-change conditions typical of urban expansion. In the expansion-only comparison, the statistical learning framework LEARN provides the strongest baseline performance (FoM ≈ 0.20), exceeding MOLUSCE (0.07), FUTURES (0.01), and SLEUTH (0.10).

We then extend the task from binary land conversion to predicting the continuous build-up surface. A random forest baseline that models built-up change directly achieves FoM ≈ 0.50 in Accra. Building on this, we implement a two-head U-Net that jointly estimates (i) the likelihood of expansion and (ii) the magnitude of build-up increase, with constraints to keep predicted change non-negative and spatially plausible. This neural approach performs best overall (FoM ≈ 0.65), improving substantially on both classical UGM baselines and the random-forest model.

Overall, results indicate that modeling build-up as a continuous surface—and explicitly coupling expansion with magnitude via neural networks—can markedly improve change-prediction skill in fast-growing cities, while remaining compatible with scenario-consistent urban forecasting frameworks.

How to cite: Noi, E., Hawker, L., Carioli, A., Espey, J., Hilton, J., and Tatem, A.: From Classical Urban Growth Models to Data-Driven Methods: Predicting Urban Expansion and Built-Up Intensity in Accra, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11681, https://doi.org/10.5194/egusphere-egu26-11681, 2026.

With the rising level of the Seine River during hurricane Kirk, the city of Alfortville (Paris area, France) was facing a major concern. If the water level goes over 6,5m high, 97% of the city will be flooded in 2 days and the decision makers will have to manage the evacuation of 45.000 inhabitants. The massive evacuation of the population in case of a major flood in the Paris area remains a major challenge for emergency managers.

This presentation introduces the results of an agent-based model designed to simulate evacuation behaviors in response to different types of flooding across three territories in the Paris metropolitan area. The model, built using the NetLogo environment, is part of the Paris-Area Flood Evacuation (PAFE) project. We constructed a synthetic population using seven socio-demographic variables, calibrated to match census data and spatially distributed in a realistic way across households in each territory. Individual evacuation decisions were informed by a large-scale empirical survey (n = 5,000), with agents’ responses linked to their socio-economic profiles. Finally, the model was refined in collaboration with local experts and decision-makers who have direct experience with past flood events in the region.

How to cite: Santoni, V., Rufat, S., Enderlin, E., and Lhomme, S.: Refining Flood Evacuation ABM with local stakeholders in the Paris Area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12217, https://doi.org/10.5194/egusphere-egu26-12217, 2026.

Extreme heat exacerbated by climate change is one of the greatest threats to the global sports industry. There are two contrasting seasonal challenges facing the sports industry. In the case of winter sports, increasing temperatures due to climate change lead to reduced natural snow cover and ice formation, as well as causing artificial snow and ice to melt. Meanwhile, the effect of extreme heat on athletes impacts summer sports, with high temperatures causing exertional heat illnesses (EHI).

The impact extreme warming is having on winter sports in particular, is already prevalent. Recent Winter Olympic Games have been severely impacted by extreme warming events, such as heat waves in the case of Sochi 2018, and as host locations are selected up to a decade before hosting, significant changes can occur within that timeframe. 

This research examines the last 30 years of temperature and snow depth in order to evaluate the feasibility of minimum snow depth requirements occurring naturally in the location of Cortina d'Ampezzo for the upcoming Winter Olympic and Paralympic Games in 2026. Subsequently, a selection of individual CMIP6 models for the next 50 years are analysed to evaluate the feasibility of this location continuing to host major sporting events that require snow depths for athlete safety, and whether this can be facilitated by natural snow alone or if artificial snow will be required.  This analysis involved the creation of a snow model in R to estimate future snow accumulation and melt in the region.

Additionally, due to the Olympics and Paralympics occurring in this venue in February and March 2026 this study is in a unique position to report from the event itself and evaluate how the previous 30 years of observations link with the reality on the ground. There has also been an opportunity to complete a mixed-methods study adding a layer of human experience by completing surveys and interviews of athletes and coaches competing in these games. As well as this the quantitative and qualitative data can be brought together in an ArcGIS StoryMap in order to illustrate whether Cortina d’Ampezzo can still host the Winter Olympic Games in the future.  

This research has the potential to expose the need for adaptation of sports infrastructure and sporting regulations to deal with the threat of extreme heat as a result of climate change.

How to cite: Kielt, A.: Sport adaptation to extreme heat in a warming world: Can Cortina d’Ampezzo continue to host the Winter Olympic Games?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19318, https://doi.org/10.5194/egusphere-egu26-19318, 2026.

The Royal Netherlands Meteorological Institute (KNMI) recently published nine different climate risk storylines to prepare for climate hazards in the current and near future climate. This study specifically zooms in on the climate risk storyline for a heatwave in Amsterdam. The summer of 2018 was exceptional, leading to the first code Orange for extreme heat. In this study we investigate whether, and how, the heatwave of 2018 could plausibly have evolved into a more extreme event. Using ensemble forecasts from ECMWF, we identify an alternative but physically consistent meteorological evolution in which the cooling front of late July 2018 did not reach the Netherlands. This alternative scenario, termed ‘Heatwave XL’, is dynamically downscaled using the regional climate model RACMO, with corrections for model bias. Urban heat island diagnostics are applied to derive spatially explicit heat exposure across Amsterdam. Sectoral impact knowledge from impact partners is then integrated to assess potential societal impacts and cascading effects. The Heatwave XL storyline results in several additional days of extreme daytime temperatures exceeding 35 °C, combined with persistently hot nights, likely exacerbating societal impacts already seen in 2018. This case demonstrates the value of storyline approaches for stress-testing preparedness and supporting anticipatory decision-making under uncertainty in a warming climate.

How to cite: van Voorst, L. and Pereira Marghidan, C.: Could the 2018 Amsterdam heatwave have been more extreme? A climate risk storyline of plausible extreme heat, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8221, https://doi.org/10.5194/egusphere-egu26-8221, 2026.

Event storylines are a variant of storylines and can be used to explore the consequences of low-likelihood high impact events. In particular, they provide the basis for a realistic emergency operations center exercise: stakeholders and scientists can run through different - also management - scenarios and asseess their complex and cascading risks and costs.

Different approaches to implementing event storylines exist; most are based on some variant of the pseudo global warming approach: an observed event is simulated under the actual (boundary) conditions and under modified boundary conditions representing selected scenarios. The simulations can then be fed into quantiative impact models and further be used for qualitative assessments. But this (in theory) very elegant approach comes along with several challenges in its practical implementation.

Here we use the example of a severe landslide event in Southern Austria to illustrate these challenges and present solutions. Heavy rainfall, caused by a slowly moving cut-off low and falling on saturated soils, triggered at least 952 landslides and resulted in substantial damage of infrastructure and buildings. To model the event, we combine kilometer-scale regional climate model simulations with a statistical landslide model, trained on a comprehensive dataset of observed landslide, meteorological, geological, topographical and vegetation data. We simulate the event under present, observed boundary conditions, as well as under modified conditions representing different global warming levels as simulated by global climate models. The actual implementation of changes in boundary conditions, however, is not a priori clear. Also, even though the event is well simulated, it is dislocated compared to observations by a few tens of kilometers. This dislocation is of the same order of magnitude as the area affected by landslides and thus makes a direct use of the simulations for driving the landslide model unfeasible for representing a specific event. 

In a set of sensitivity studies, we first explore the influence of (1) simulating the event with climatological boundary conditions over a large domain with spectral nudging vs. event-type specific boundary conditions over a small domain without spectral nudging; and (2) imprinting altitude dependent or constant changes in different atmospheric variables, from temperature only to temperature, humidty and sea level pressure. The results depend strongly on the implementation. However, a process-based analysis reveals that only the small-domain variant with sea level pressure and consistent altitude-dependent changes in temperature and relative humidity simulates physically plausible changes. Second, we develop a delta change approach, which (1) replaces temporal by spatial averaging to calculate change factors, and (2) applies changes separately to precipitation at different time-scales.  Finally, we discuss the relevance of carefully defining the event in time, including preconditioning by antecedent precipitation which may change in a warming climate, and how changes in these preconditions can be simulated.   

Our study demonstrates the great potential of event storylines for risk assessments, but also highlights the need for a range of critical choices and post-processing steps that need to be carefully considered to arrive at plausible results. 

How to cite: Maraun, D., Lezameta, L., and Truhetz, H.: Challenges in using event storylines for climate risk assessments. The example of a severe landslide event in Austria., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17887, https://doi.org/10.5194/egusphere-egu26-17887, 2026.

We present a harmonized dataset of globally comprehensive up-to-date emissions trajectories and their emulated climate outcomes, developed to support the ScenarioMIP experiment within CMIP7. Drawing from a set of around 90 candidate scenarios, a small subset of 7 marker scenarios is selected to span a wide range of emissions and climate outcomes to be simulated by earth system models (ESMs) in the AR7 Fast-Track.

These scenarios are calculated using seven Integrated Assessment Models (AIM, COFFEE, GCAM, IMAGE, MESSAGE-GLOBIOM-GAINS, REMIND-MAgPIE, and WITCH) and are based on newly updated socioeconomic pathways (SSPs). 

In CMIP6, ESM projections have mainly been driven by changes in atmospheric concentrations. CMIP7 prioritises emissions-driven climate projections, meaning the harmonization and spatial distribution of emissions are of increased importance.

For CMIP7, we combine multiple strands of previous work into one workflow that includes: (1) compiling a common historical emissions dataset, for each IAM region, and all climatically relevant emissions species, (2) harmonizing sectoral emissions pathways to 2023 emissions, (3) generating harmonized gridded emissions data, (4) running updated simple climate models to emulate the range of possible climate outcomes of the emissions pathways.

In this presentation, we present: the workflow, the new CMIP7 scenario set, and how it compares to the CMIP6 scenarios.

The data presented are meant support earth system modelling and impact assessment across the CMIP7 Assessment Fast-Track and beyond, including model intercomparison projects such as ISIMIP, AerChemMIP, and CDRMIP, and in doing so, support upcoming IPCC assessments.

References

• Van Vuuren, D., O’Neill, B., Tebaldi, C., Chini, L., Friedlingstein, P., Hasegawa, T., Riahi, K., Sanderson, B., Govindasamy, B., Bauer, N., Eyring, V., Fall, C., Frieler, K., Gidden, M., Gohar, L., Jones, A., King, A., Knutti, R., Kriegler, E., Lawrence, P., Lennard, C., Lowe, J., Mathison, C., Mehmood, S., Prado, L., Zhang, Q., Rose, S., Ruane, A., Schleussner, C.-F., Seferian, R., Sillmann, J., Smith, C., Sörensson, A., Panickal, S., Tachiiri, K., Vaughan, N., Vishwanathan, S., Yokohata, T., Ziehn, T., 2025. The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7). EGUsphere 1–38. https://doi.org/10.5194/egusphere-2024-3765

How to cite: Kikstra, J., Högner, A., Zecchetto, M., Ahsan, H., Gidden, M., Riahi, K., Smith, C., Smith, S., and Nicholls, Z.: CMIP7-ScenarioMIP emissions set and probabilistic climate outcomes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14856, https://doi.org/10.5194/egusphere-egu26-14856, 2026.

Stress-testing has long been a fundamental practice in fields like finance to evaluate systemic resilience under extreme conditions. Climate scenarios however typically feature average projections and expected impacts, often neglecting critical questions such as “What if we face extremes at the upper ends of climate uncertainty, or at what levels are critical thresholds breached?”. The SPARCCLE project seeks to fill this gap by integrating stress-testing approaches into climate scenario analysis, thereby exploring the implications of extreme, but plausible climate futures under a 1.5° and a current policy scenario.

For this purpose, the SPARCCLE project has actively engaged a diverse set of stakeholders from the energy, health, and finance sectors to co-develop three stress-testing storyline-and-simulation approaches. These  were translated into narrative aspects of interest on various climate and socioeconomic European challenges into quantified scenarios. These scenarios illustrate conditions that stretch the limits of existing adaptation and risk management frameworks.

Through structured webinars, a 2-day workshop with 30 participants including stakeholders and climate modellers, and ongoing iterative discussions to prompt aspects of interest then validate quantifications, we identified key vulnerabilities and cascading impacts of extreme climate events on critical sectors. The result are three storylines focusing on (i) Europe under heat stress, (ii) Water – too little and too much and (iii) Europe in a fragmented world. Our interdisciplinary collaboration with modelling experts encompasses methodologies ranging from simple climate models to impact models to integrated assessment models (IAMs), ensuring alignment between stakeholder-driven storylines and cutting-edge scientific insights.

In this presentation, we will provide a comprehensive overview of our co-development process, detailing our methodological framework, present the three storylines and the transition of qualitative narratives into quantitative multi-model experiments. We will highlight challenges encountered and solutions devised throughout this journey. Furthermore, we will discuss how the stress-test scenario exercise can contribute to improved decision-making both for adaptation and mitigation.

How to cite: Menke, I., Schmidt, S., Byers, E., and Zhu, Q.: From Narratives to Quantification: Co-Developing Stress-Test Scenarios for Climate Adaptation and Mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13846, https://doi.org/10.5194/egusphere-egu26-13846, 2026.