- early warning systems and their evaluation
- physical climate science knowledge gaps that affect decision making
- risk management in the financial and insurance sectors
- impact-based forecasting of weather extremes
- cross-boundary and trans-lateral effects
- assessment of dynamically varying vulnerabilities
- data-driven approaches using machine learning, and
- storyline approaches to risk understanding.
The focus of this session is on interdisciplinary approaches to translating physical science into decision-relevant information.
Decision-makers depend on climate projections and information on extreme weather to assess and manage complex climate risks. While physical climate science has made substantial progress in characterising changes in hazards, translating this information into effective adaptation and risk reduction strategies remains challenging. A key reason is that climate change adaptation and disaster risk reduction (DRR) are often addressed through separate analytical frameworks, despite the fact that real-world climate risk emerges from their combined influence on hazards, vulnerability, and exposure.
Addressing long-term climate change risks while simultaneously managing short-term risks from extreme and compound events requires integrated approaches that move beyond hazard-centred assessments. Climate risks are shaped by dynamic interactions between multiple hazards, evolving vulnerability, and exposure patterns that are highly context-specific. Understanding these interactions is essential for producing climate risk information that is meaningful for decision-making across spatial and temporal scales.
This presentation explores how interdisciplinary approaches can support more decision-relevant climate risk assessments by combining insights from physical climate science, disaster risk management, and social science. It highlights the need for both top-down and bottom-up perspectives, and for the integration of quantitative and qualitative evidence, to better capture adaptation, adaptive capacity, and vulnerability dynamics in climate risk analysis.
Examples are drawn from recent efforts to improve the representation of adaptation in climate impact assessments, including the use of global proxy indicators of adaptive capacity, as well as from bottom-up research that reveals how actors on the ground understand and respond to risks arising from multiple interacting hazards. The presentation also discusses the role of emerging data sources, such as Earth Observation, in identifying vulnerable populations in data-scarce regions and supporting more equitable targeting of adaptation and risk reduction efforts.
Together, these perspectives highlight the importance of integrated, interdisciplinary approaches for producing climate risk information that is meaningful across policy and practice.
How to cite: van Maanen, N.: Integrating Climate Change Adaptation and Disaster Risk Reduction for Decision-Relevant Climate Risk Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18280, https://doi.org/10.5194/egusphere-egu26-18280, 2026.
Wildfire is a multi-dimensional hazard, impacting both human livelihoods and ecosystem function. Beyond wildfire prediction and containment, post-fire reconstruction is a major management challenge. With shifting and novel fire regimes, post-fire recovery represents a complex risk-management challenge where decisions made under high uncertainty have long-term implications for systemic resilience.There is an urgent need for tools which allow land managers to explore their options in an accessible, systematic and transparent way.
Here, we present a joint effort between the Schmidt Center for Data Science and Environment and the U.S. National Parks Service to design a decision-support platform, enabling park managers to create future management scenarios based on current understanding of climate futures to guide their decision making. Using the Mojave Desert ecosystem in Southern California as a case study, we discuss our collaborative co-design process, technical infrastructure and scientific reasoning in translating high-performance vegetation modeling into actionable policy insights
More specifically, we present josh, an open-source, domain-specific scripting language linked to a high-performance simulation engine. We illustrate how josh can be used to design vegetation models and management intervention for a range of ecosystems, integrate different high-resolution future climate projections and quantify risk and uncertainties through running large, stochastic ensemble simulations. The platform is freely available, open-source, and runs in any web browser, as well as on distributed computing systems; providing a transparent and accountable tool for evidence-based adaptation planning.
How to cite: Layritz, L. S., Zomer, M., Graver, N., Gondek, N., Anderson-You, A., Pottinger, S., Weltman-Fahs, M., and Boettiger, C.: Integrating future climate projections into post-fire management: A stochastic decision-support toolbox for adaptation in arid ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9645, https://doi.org/10.5194/egusphere-egu26-9645, 2026.
Understanding climate change risks requires moving beyond hazards alone and examining how climatic stress propagates through coupled natural and human systems. Semi-arid coastal basins are particularly exposed to these dynamics, where prolonged drought, intense human water use, and sensitive downstream ecosystems interact to shape complex and often unintended risk trajectories. Central Chile provides a compelling example, having experienced a nearly 15-year megadrought that has profoundly altered hydrological, ecological, and socio-economic conditions.
In this study, we explore how climate-driven water scarcity is transmitted across semi-arid coastal basins, and how human adaptation responses reshape both short- and long-term risks. Using an integrated socio-ecological framework, we combine satellite remote sensing, hydroclimatic records, land-use and census data, and water rights information. Indicators include precipitation, streamflow and groundwater depth, standardized drought indices, vegetation dynamics derived from NDVI, urban expansion inferred from night-time lights (VIIRS), and surface water changes in small coastal lagoons quantified using NDWI.
Our results reveal contrasting adaptation pathways across managed and unmanaged systems. Irrigated agriculture shows a high degree of apparent resilience, maintaining vegetation productivity during prolonged drought through intensified groundwater use and technological adaptation. However, this response is closely linked to accelerated groundwater depletion, streamflow collapse, and downstream ecological degradation, illustrating a clear case of maladaptation driven by short-term productivity gains. In contrast, natural shrublands and forests respond more directly to hydroclimatic variability, with forest systems exhibiting delayed and potentially threshold-like responses under sustained drought conditions.
Coastal lagoons emerge as sentinel systems that integrate cumulative basin-scale stress. Satellite observations document a shift from persistent ocean connectivity to prolonged inlet closure during the megadrought, alongside shrinking water surfaces and signs of regime change at the land–sea interface. Overall, our findings highlight how uneven adaptation capacity and sector-specific responses can amplify cascading climate risks, underscoring the need for integrated, basin-scale adaptation strategies that explicitly consider cross-system feedbacks, ecological thresholds, and governance constraints.
How to cite: Vicuña, S., Gomez, R., Bravo, V., Vargas, J., Gutierrez, A., Williams, M., Gaxiola, A., Leray, S., Melo, O., Gonzalez, D., Zuñiga, P., and Meza, F.: Assessing the complex nature of climate change risks in semi-arid coastal basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14617, https://doi.org/10.5194/egusphere-egu26-14617, 2026.
Energy transmission networks represent the backbone of modern societal functioning. With a changing climate system, the resilience of this critical infrastructure has become a paramount concern for grid operators in response to extreme weather events. However, assessing the systemic risk of such networks remains a significant challenge. Traditional climate impact and risk assessments often evaluate components in isolation, thus, failing to capture the complex, interconnected dependencies of high-voltage transmission, making it difficult for decision-makers to implement an informed, systemic process for risk disclosure, climate adaptation, and resilience strategies.
Accounting for the systemic perspective of energy transmission networks, a complex network-based clustering approach, informed by climate risk and damage impact data, is applied to evaluate the exposure of interconnected transmission systems. Utilizing the Transpower open network asset dataset for New Zealand’s national transmission network to construct a graph-based data model. By computing local clustering coefficients to quantify structural meshing and redundancy, we identify distinct functional clusters and rank components according to their systemic criticality. This enables the translation of complex physical network topology and historical vulnerability into a prioritized hierarchy of grid exposure, identifying which nodes are most vital to maintaining stability during extreme weather events.
The efficacy of this approach is demonstrated using a case study of New Zealand’s national transmission network. Our results showcase how neural networks can delineate high-risk clusters and identify linchpin assets that, if compromised by extreme weather events, would cause disproportionate systemic and cascading failures. By providing a spatially explicit ranking of grid criticality, this data-driven approach offers a scalable tool informing climate impact and risk assessments.
The interdisciplinary research presented exemplifies the translation of climate and data science into decision-relevant information. It provides a robust methodology for assessing dynamically varying grid exposure, ultimately supporting the development of more resilient energy infrastructure and providing a template for advanced climate impact and risk understanding in interconnected systems.
How to cite: Remke, T. and Ferrer, J.: Enhancing Power Grid Resilience: A Complex Network Approach to Mapping Criticality and Climate Risk in Interconnected Energy Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19307, https://doi.org/10.5194/egusphere-egu26-19307, 2026.
Every city is unique and complex. Examples include its geography, decision-making context, and climate-related risk profile. At the same time, each city shares similarities with other cities. The same applies to counties. Complex climate-related risks are increasing in cities in the EU. This makes the transfer of effective adaptation and mitigation measures between cities increasingly important, especially as time and funding for local case studies are limited. It is uncontroversial that transfer between similar cities, or similar counties respectively, is more probable. Systematic approaches to support this transfer are rare.
One way to reduce complexity in the world, and across such units of analysis, is by looking for patterns. Using a well-established data-driven methodology with a cluster analysis at its core, we identify and analyze recurrent patterns of multiple climate-related risks across urban areas, and derive what urban planning and design can do about it. We do this for 1152 NUTS-3 (county level) units covering over 99% of the EU area using over ten spatially explicit datasets on exposure to climate-related exteme events (for drought, heat, landslide, wildfires, air pollution, and flooding types) and exposure to sea-level rise.
In the resulting spatially explicit typology, each of the eight clusters, or groups, consists of NUTS-3 units which have similar combinations and degrees of multiple climate-related hazards. Each group was then comprehensively statistically analyzed and characterized. Then we derived and suggested combinations of areas for action and adaptation measures for decision-making in each group to focus on for reducing combined climate-related risks. On a city-and county level this supports urban planners and authorities, on a regional level political decision-making, and on an EU level strategically scaling up climate action.
For example, one group of NUTS-3 units exhibits the most pronounced dryness, alongside high heat hazard and highest wildfire exposure, in parts of France, Spain, Portugal, Italy, Croatia, Romania, and Bulgaria. On this basis, we suggest integrating measures from action areas such as heat action plans, nature-based solutions to multiple hazards simultaneously, as well as public health measures, water management and science-based risk assessments and subsequent adaptation plans.
The climate-risk related typology is supplemented by three further EU-wide NUTS-3 level typologies based on 5-8 datasets each: contribution to mitigation, urban morphology, and capacity for action. This allows for a highly detailed and interdisciplinary storyline for understanding risk in each county, and county group, through a lens of urban planning.
The study was conducted in the Horizon EU project UP2030 (Urban Planning 2020, https://up2030-he.eu/). The results can be found here at https://urbanplanningfor2030.eu/form/urban-typologies. The methodology is interdisciplinary, drawing from climate risk assessment, governance, geography, and urban planning and dialogues between ten urban authorities. We also show that the methodology is also applicable to mitigation problems, and is applicable to other spatial units, such as ecosystems, conservation areas, or grid cells.
How to cite: Sterzel, T., Bartsch, J., Hossain, K., Tougas, F., and Walther, C.: Typology of climate risks for scaling up urban planning-based adaptation in the EU, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22149, https://doi.org/10.5194/egusphere-egu26-22149, 2026.
Decision-makers across public and financial sectors increasingly require robust, decision-relevant information on climate hazards associated with extreme weather events. The Copernicus Climate Change Service (C3S), implemented by the European Centre for Medium-Range Weather Forecasts (ECWMF) on behalf of the European Commission, facilitates the development of adaptation and mitigation strategies for society in the face of climate change. Among the different components of the service, C3S supports the development of climate hazard information to strengthen physical climate risk assessments, including applications for the European Investment Bank. The service addresses methodological challenges related to the selection, combination, and interpretation of climate datasets and scenarios across sectors. The work promotes interdisciplinary integration between climate science, risk assessment, and decision-making communities, supporting more robust and actionable climate risk analyses. This contribution highlights key methodological elements and lessons relevant for advancing integrated climate risk approaches.
How to cite: Cagnazzo, C., Roherl, R., Smith, B., Colledge, F., Leader, S., Wade, S., Spillar, M., Romero, L., Peña-Izquierdo, J., Hofmann, S., Jimenez, I., and Moreno, P.: C3S Climate Services for physical climate risk assessment in the financial sector, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22213, https://doi.org/10.5194/egusphere-egu26-22213, 2026.
Climate change is altering the distribution and frequency of extreme weather events, threatening both the global economy and financial sector stability. Investors, regulators, and public institutions increasingly seek to understand the connection between climate and financial risk, yet few global integrated frameworks exist that link physical hazards to asset exposure, damage, economic disruption, and ultimately financial loss. To address this critical knowledge gap and building upon initial efforts that were previously constrained to the United States, First Street has developed and implemented a comprehensive and integrated global climate risk modeling framework to generate granular, asset-specific hazard and associated probable loss estimates across multiple perils, including flood, wildfire, extreme heat, severe convective storms, and wind storms. The models and methodologies underpinning these outputs are grounded in Open Science principles, with comprehensive descriptions published in the peer-reviewed literature and accessible online, facilitating transparency and scientific reproducibility. These risk data are subsequently translated into financial impacts through Climate Risk Financial Modeling, applying asset-level hazard exposure with digital twin approaches combined with advanced loss modeling, providing inputs for decision-making across various private and public sectors. Collections of assets are also assessed using catastrophe modeling principals to allow peril-specific and multi-peril estimates of portfolio-scale probable losses under different climate scenarios. Aggregation of risk metrics at higher administrative units enables socioeconomic modeling, including projections of climate migration and economic impacts to aid in interdisciplinary risk assessment. This presentation will summarize the latest global climate risk and loss projections, particularly concerning the quantification of climate-related financial risk.
How to cite: Maneta, M., Flamig, Z., Porter, J., Kim, J., Lammers, M., Freeman, N., Amodeo, M., and Kearns, E.: An Interdisciplinary Approach to Asset-Specific Climate Risk Financial Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15187, https://doi.org/10.5194/egusphere-egu26-15187, 2026.
Decision-making in coastal and urban environments increasingly depends on the ability to navigate complex and interacting climate risks. Yet, relevant knowledge is often scattered across disciplines, institutions, data repositories, and policy documents, making it difficult to access and use in practice. The amphibious city of Venice (Italy) is an exemplary case: its unique environmental setting, long history of climate exposure, and dense legacy of scientific research and monitoring coexist with highly complex governance and decision-making processes.
Here we present the International Centre for Climate Change Research and Studies (CSRCC) and its recently launched activities, highlighting the advanced applications being developed to foster the systematization of interdisciplinary knowledge.
The CSRCC brings together several interconnected activities with the aim of serving as a bridge between science and decision‑making in the Venetian context. These include the development of an advanced meta-database, designed to organize and explore not only climate and environmental data, but also research outputs, monitoring programmes, regulations, plans, and policy documents relevant to climate mitigation, adaptation, and resilience. These multi-disciplinary connections/relations enable us to build artificial intelligence applications to improve queries, allowing users to extract inter-connected knowledge which is critical to understand and support decision-making in complex environments.In parallel, the CSRCC is preparing an IPCC-like local Climate Assessment Report, aimed at synthesizing existing knowledge in a transparent and decision-relevant way. Ongoing activities also include research on long-term sea-level rise and lagoon evolution, providing historical and geological context for current and future risks, as well as the integration of the Centre’s work within broader European research and coordination initiatives.
Using Venice as a testbed, we discuss how assessment-oriented and metadata-driven approaches can help translate fragmented climate knowledge into usable information for successful mitigation and adaptation strategies, and how this experience may inform similar efforts in other coastal and urban settings.
How to cite: Rova, S., Ardenghi, N., Cerrone, C., Longato, D., Palmieri, L., Valley, S. G., and Barbante, C.: From fragmented climate knowledge to decision-relevant information: the approach CSRCC (International Centre for Climate Change Research and Studies) for the Venice Lagoon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23069, https://doi.org/10.5194/egusphere-egu26-23069, 2026.
Heatwaves pose an increasing threat to public health under climate change. Despite evidence that health systems in high-latitude countries are insufficiently prepared for extreme heat, few studies have investigated the state-of-the-art deep learning (DL) models to forecast heat-related morbidity at seasonal lead times. This study develops and evaluates a multivariate, multi-step impact-based forecasting framework across Sweden for predicting heat-related morbidity using Neural Basis Expansion Analysis for Time Series (N-BEATS) models. N-BEATS models are developed and tested under recursive and multi-input–multi-output (MIMO) multi-step forecast strategies and compared with statistical baselines (ARIMA, naïve seasonal) and classical DL model (Long Short-Term Memory (LSTM)). Forecasts are generated using morbidity counts alone and in combination with exogenous covariates (Heat Wave Index and the number of individuals with respiratory diseases) while local and global modeling approaches are examined.
Results show that N-BEATS with both covariate and local modelling strategy significantly outperforms all baseline models with the lowest MAE, RMSE, and MASE values. N-BEATS shows greater data efficiency with iteratively refined residuals through fully connected backcast and forecast stacked blocks compared to LSTM, particularly when there is an extreme morbidity peak. Individually trained local N-BEATS models are more effective than the cross-learning global N-BEATS, even with similar seasonal peaks and lower data quantity. Regional differences in climate, hydrology, and demographics could hinder the effectiveness of global models and underscore the importance of localized adaptation plans and measurements. Models may also underperform during unprecedented periods, such as during the COVID-19 pandemic in 2021. The underperformance may have resulted from disruptions in healthcare during COVID, behavioral change from seeking healthcare, and selected covariates didn’t capture healthcare system capacity. Future study could be improved by testing model performance to incorporate a covariate that reflects healthcare system capacity, such as service load to enhance model’s robustness to similar system level shock.
The study offers a concrete step toward operational impact-based early warning systems by enabling national agencies to anticipate heatwave burdens when a seasonal heatwave alert is issued. By coupling hazard forecasting with health impact prediction, this work supports the development of impact-based early warning systems tailored to the growing risks of extreme heatwaves. Integrating morbidity forecasts into heat-health action plans can support public health agencies in proactive resource allocation, risk communication, and preparedness planning.
How to cite: Kan, J.-C., Vieira Passos, M., Destouni, G., Barquet, K., S.S. Ferreira, C., and Kalantari, Z.: Advancing Heat-Related Impact Forecast Using Multivariate Deep Learning Models , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1633, https://doi.org/10.5194/egusphere-egu26-1633, 2026.
Weather-related hazards represent a major source of risk for the insurance sector. However, insurance risk assessment still largely relies on isolated events, single hazard analyses, and probabilistic loss metrics wich provide a limited understanding of the recurrent weather conditions that drive insurance losses. We introduce an impact-driven and decision-oriented framework to identify weather regimes directly conditioned on insurance loss occurrence, using ERA5 reanalysis data to bridge atmospheric drivers and observed impacts over France. This framework is calibrated on high-impact loss days since 1998 to extract robust weather regimes and is evaluated across the full observation period to assess their loss contribution and spatio-temporal expression. This approach reveals a limited set of recurrent, spatially organised weather regimes associated with distinct loss signatures. We provide a weather regime-based storyline linking atmospheric drivers and observed insurance losses - offering a coherent framework to interpret loss variability, supporting impact attribution, and informing risk interpretation and decision-making at the insurance portfolio scale.
How to cite: Hénaff, Q., Poletti, A., and Blaquière, S.: Atmospheric drivers of weather-related insurance losses using ERA5 reanalysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2247, https://doi.org/10.5194/egusphere-egu26-2247, 2026.
Affiliation: CIMA - Centre for Marine and Environmental Research, University of Algarve, Aquatic Research Network (ARNET)
Address: University of Algarve, Campus of Gambelas, 8005-139 Faro, Portugal
ABSTRACT
In 2021, approximately 2.4 billion people lived in coastal areas. These populations, along with their environments, face escalating risks from climate hazards and ongoing development. Under a pessimistic perspective of increasing frequency and intensity of extreme events, due to climate change, there is a need to implement disaster risk reduction (DRR) measures. Communicating risks and engaging with local populations should be part of DRR plans to ensure the safety of coastal communities. Communication about coastal risk, and about the coast more generally, should be made strategically, efficiently and with the intention to build coastal literacy. However, the definition of coastal literacy is still an ongoing process by the team of SYREN Project – a research initiative committed to improving coastal risk communication. To date, the concept was framed under seven key principles, namely: coasts are unique and valuable (Principle 1); composed of interconnected parts (Principle 2); constantly changing over time (Principle 3); influenced by human activities and vice-versa (Principle 4); inherently hazardous and capable of posing risks (Principle 5); affected by climate change (Principle 6); and there is shared responsibility to look after coasts, for present and future generations (Principle 7).
This work presents the results from a literature review on coastal literacy principle 7, particularly the coastal actors, focused on enhancing the understanding of responsibilities involved in ‘looking after’ coasts. The process allows for the identification of key actors responsible for ensuring that coasts are managed in ecological, economic and socially sustainable ways. This includes recognising the differing roles and stakes of groups such as residents, policy administrators, property developers and others.
Two distinct forms of responsibility related to looking after coasts were identified. The first pointed to actors responsible for causing or amplifying damage, such as coastal development companies, hard infrastructure project builders, and major carbon-emitting industries. The second concerned actors who are or feel responsible for protecting and managing coasts, including communities and governmental bodies. Finally, the review considered challenges of responsibility across regional and temporal scales. It emphasised that coastal management strategies must go beyond local problem-solving to incorporate cross-border, recognitional, and intergenerational justice, highlighting that responsibility extends across regions and toward past and future generations. Overall, the analysis of actors and responsibilities helps clarify what it means to have a ‘shared responsibility’ for looking after coasts.
Acknowledgements: This study contributes to the project SYREN (Ref. ALGARVE-FEDER-00853600-SYREN-17135), funded by Fundação para a Ciência e a Tecnologia, Programa Operacional Regional do Algarve, and Programa Operacional Regional de Lisboa.
How to cite: Dann Ruiz, L., Matias, A., and Carrasco, R.: Actors and Responsibilities in Coastal Risk: A Literature Review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1164, https://doi.org/10.5194/egusphere-egu26-1164, 2026.
Climate change is generating increasingly complex risks for socio-economic systems through the interaction of climate extremes, development trajectories, and adaptive capacities. However, dominant vulnerability–resilience frameworks often assume that economic development monotonically reduces climate risk, thereby overlooking nonlinear and regime-dependent dynamics. This study adopts an interdisciplinary risk perspective to examine how tourism systems respond to complex climate risks shaped jointly by climate extremes and economic conditions. Focusing on western China as a climate-sensitive and economically heterogeneous region, we develop a threshold-governed tourism vulnerability–resilience framework that integrates climate exposure, sensitivity, adaptive capacity, and governance readiness. Using panel data from 13 regions over the period 2003–2023, we apply panel threshold regression models to identify regime shifts in tourism responses across different levels of climate risk and economic development. The results reveal pronounced nonlinear dynamics. Below a critical economic threshold, tourism systems exhibit high sensitivity to climate extremes, with exposure acting as a dominant constraint on tourism performance. Beyond this threshold, the functional role of exposure changes and becomes increasingly mediated by governance capacity and adaptive investment. Climate-risk thresholds further amplify these effects: under high-risk regimes, negative exposure impacts intensify sharply, while the marginal effectiveness of adaptive capacity increases significantly. These findings demonstrate that tourism vulnerability and resilience are governed by explicit thresholds rather than linear development pathways. By revealing regime-dependent risk mechanisms in a coupled human–environment system, this study advances interdisciplinary understanding of complex climate risks and provides insights for designing development-stage- and risk-specific adaptation strategies.
How to cite: Zhou, L., Bao, H., Li, J., Wang, Q., and Sun, Z.: Threshold-governed dynamics of tourism vulnerability and resilience under climate extremes and economic development, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4424, https://doi.org/10.5194/egusphere-egu26-4424, 2026.
Extreme precipitation is projected to intensify under climate change, yet global and regional climate model outputs are typically provided at resolutions of several to tens of kilometers, limiting their ability to represent localized precipitation structures and extremes. This study aims to develop an ensemble-learning framework for downscaling coarse precipitation fields to high-resolution fields. The proposed framework ensembles a generative adversarial network (GAN), a convolutional encoder–decoder architecture (U-Net), and a diffusion model to avoid single-model bias and to quantify downscaling uncertainty through ensemble spread. High-resolution gridded precipitation data from the Korea Meteorological Administration (KMA) serve as a reference for ensemble learning. Performance is evaluated through a reconstruction experiment in which high-resolution precipitation fields are artificially coarsened, downscaled, and compared with the original data using root mean squared error, bias, and an extreme-focused metric (the 95th percentile). The trained framework is applied to 25 km regional climate projections under Shared Socioeconomic Pathway (SSP) scenarios, generating 1 km precipitation projections for the Republic of Korea through 2100. Results show improved representation of spatial patterns and extreme statistics relative to individual models, while providing uncertainty bounds for projected extremes. Future work will extend the framework so that the downscaled precipitation data are compatible with geological data (e.g., terrain) at tens-of-meters resolution, enabling analyses of how climate risks influence geohazard risks.
How to cite: Kim, C., Jang, M., Ahn, Y., Chae, M., Lee, J., Thanh, H. V., Ryu, D.-W., Jo, S., and Min, B.: Precipitation downscaling based on ensemble learning for climate risk assessment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16410, https://doi.org/10.5194/egusphere-egu26-16410, 2026.
Decision-makers across Europe are increasingly challenged by the escalating impacts of climate change, including extreme weather events. Addressing these challenges requires interdisciplinary and actionable approaches to translate climate science into decision-relevant information, especially at local and regional level. Within the EU Horizon project RESIST, we present a multi-regional climate risk assessment co-developed with stakeholders in three diverse European regions—Finland, Portugal, and Ukraine.
The assessment process began with a structured needs analysis through workshops and interviews with regional authorities to identify sector-specific vulnerabilities. Using an extensive climate database, we evaluated key hazards such as temperature extremes, heavy precipitation, droughts, and floods, including projected changes under different IPCC scenarios. Building on these insights, we applied an established conceptual and methodological framework to conduct integrated climate risk assessments.
A key strength of this approach is the combination of quantitative and qualitative data, geospatial analyses, and expert knowledge to produce location-specific risk profiles addressing local priorities. This stakeholder-driven process also enabled the inclusion of cascading effects and sectoral impact analyses across infrastructure, agriculture, and ecosystems, capturing dynamically varying vulnerabilities.
The outcomes identify climate risks most relevant for local actors and inform the development of context-appropriate adaptation measures using available resources. Furthermore, the approach supports cross-regional knowledge transfer by highlighting analogous risks and scalable solutions—for example, adapting heat risk strategies developed in Portugal for other heat-exposed regions.
Finally, the assessment results are designed for integration into regional digital twins, providing a foundation for multi-domain planning, from early warning enhancements to financial risk management. This interdisciplinary effort demonstrates how co-produced climate risk information can bridge the gap between physical climate science and policy needs, advancing Europe’s collective resilience to climate change.
How to cite: Bartsch, J., Hölscher, L., and Gettueva, D.: From Climate Data to Actionable Risk Information: A Co-Developed Framework for Local Climate Resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17062, https://doi.org/10.5194/egusphere-egu26-17062, 2026.
Climate change has increased the frequency and intensity of weather- and climate-related hazards, posing growing challenges to economic systems and financial stability. Recent assessments indicate that a large share of global economic losses and insurance claims can be attributed to meteorological and climate-related events. These developments have motivated increasing attention to climate risks within financial markets and regulatory frameworks. However, approaches to climate risk assessment often remain fragmented, with limited integration between physical climate processes and financial risk transmission mechanisms.
This research reviews recent research progress in financial meteorology, an interdisciplinary research area that combines atmospheric science, economics, and finance to examine the interactions between meteorological conditions and financial systems. We summarize the main pathways through which climate and weather risks affect financial institutions, distinguishing between physical risks, arising from extreme events and long-term climatic changes, and transition risks, associated with policy, technological, and market adjustments related to climate mitigation. These risks can propagate from the real economy to the financial sector through impacts on production, asset values, credit quality, and insurance losses, potentially amplifying systemic vulnerabilities.
We further review advances in climate-related financial instruments and risk management practices, including weather index insurance, catastrophe bonds, weather derivatives, and climate-related financial disclosures. International experiences suggest increasing consensus on the importance of forward-looking climate risk assessment, stress testing, and standardized disclosure frameworks. At the same time, growing demand from financial institutions has accelerated the use of meteorological data and climate information in risk evaluation, asset pricing, and insurance design.
Finally, we identify key challenges and research needs in financial meteorology. These include limitations in data availability and consistency, insufficient representation of compound and extreme events in financial models, and mismatches between climate time scales and financial decision horizons. We argue that further integration of climate science and financial analysis is necessary to improve climate risk assessment and to support effective adaptation and risk management under ongoing climate change.
How to cite: Zhu, Y. and Chen, S.: Financial Meteorology and Climate Risk: An Interdisciplinary Perspective on Physical and Transition Risks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17975, https://doi.org/10.5194/egusphere-egu26-17975, 2026.
How to cite: Wilhelm, L., Agayar, E., Aregger, M., P. Brennan, K., Bresch, D., Calanca, P., Cui, R., Gebhart, V., Germann, U., Hering, A., Schär, C., Schmid, T., Thurnherr, I., Wernli, H., and Martius, O. and the full scClim team: scClim: An interdisciplinary project for assessing hail risk and impacts across Europe in a changing climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20960, https://doi.org/10.5194/egusphere-egu26-20960, 2026.
The Tarim River is the longest inland river in China. Its headwater regions suffer from weak monitoring of meteorological conditions, snow cover, and floods, as well as relatively insufficient research on the formation mechanisms of snowmelt floods, posing significant challenges for high-precision flood forecasting and early warning. Based on the runoff generation processes in the headwater regions from 2000 to 2023, this study proposed a set of flood-influencing factors from three aspects: hydrometeorology, solar radiation characteristics, and underlying surface conditions. Principal component analysis was employed for dimensionality reduction to extract key input variables for runoff prediction models for six tributaries, namely the Kumarak River, Toshkan River, Taxkorgan River, Yarkant River, Karakash River, and Yurungkash River. The cumulative variance contributions of the first four principal components were 88.83%, 88.24%, 87.07%, 87.61%, 87.93%, and 86.48%, respectively, all exceeding 85%, thereby retaining most of the information from the original data. Four-layer neural network prediction models based on the LSTM algorithm were developed for the six tributaries. The Nash-Sutcliffe efficiency (NSE) values during the prediction period were 0.9751, 0.9573, 0.9648, 0.9929, 0.9477, and 0.9785, respectively, indicating overall satisfactory simulation performance, particularly for accurate predictions of low to medium flows below 600 m³/s. The error rates for peak flood flow predictions ranged from 5.55% to 16.72%, while the error rates for three-day flood volume predictions ranged from 2.37% to 15.76%. The errors for peak occurrence time were generally within one day. This research provides a technical reference for flood prediction and regulation in the Tarim River Basin.
How to cite: Tang, F. and Wang, Y.: An Machine Learning-Based Adaptive Prediction Model for Floods in the Headwater Region of the Tarim River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2922, https://doi.org/10.5194/egusphere-egu26-2922, 2026.
Derechos are a collection of downbursts produced by a group of thunderstorms that lead to widespread straight-line winds. They are most common in the great plains area of the USA but are increasingly being found in the North European Plains and especially in Poland and Belarus.
On 11/12 August 2017 a very strong derecho moved from south to north starting in the Czech Republic and across central Poland and on out into the Baltic Sea. The storm caused six deaths and several dozen injuries and extensive damage to utilities and buildings and to 80,000 ha of forest and 67,000 hectares of agricultural crops. The concern is that such events are likely to become more frequent in the future in the changing climate and forests in regions affected by derechos require adapted management to make them more resilient.
We tested whether a mechanistic wind damage model for forests (ForestGALES) that was originally developed for predicting damage from winter extra-tropical storms could predict the areas of damage caused by the 2017 derecho when combined with high resolution (2 x 2 km) wind field predictions from the AROME mesoscale atmospheric model. Detailed tree inventory data from the Polish National Forest Inventory (NFI) was used together with soil data as inputs to the ForestGALES model to calculate the wind speed at which damage was expected to occur for each NFI plot measured in 2016. These critical wind speeds (CWS) were then compared with the predicted wind speeds at 10 m elevation from the AROME model to give a probability of damage based on a sigmoid damage function.
The predictions of the combined models were tested using Receiver Operator Characteristics (ROC) by adjusting the damge threshold in the sigmoid function and calculating the Area Under the Curve (AUC). An AUC of 0.5 suggests no model discrimination, more than 0.7 is considered as acceptable discrimination, and more than 0.8 as excellent discrimination. For the derecho of 11/12 August using the CWS values predicted by ForestGALES and the gust speeds predicted by the AROME model an AUC of 0.858 and a model accuracy (percentage of correctly identified damaged and undamaged NFI plots) of 77.5% was achieved.
The results suggest the ForestGALES model when used in conjunction with the AROME high-resolution mesoscale model does an excellent job of identifying the forest stands most likely to be damaged. This information can be used to identify which forest stands are most resistant to the extremely strong winds found in derechos, and what characteristics of these stands made them more resistant. Such knowledge can help forest managers create more resilient forests. In addition, such a system could be used to identify the trees and forest stands most at risk of damage before the arrival of a derecho and allow emergency services to anticipate where damage is most likely to be a problem and to organise their response ahead of the storm.
How to cite: Gardiner, B., de Guerry, B., Socha, J., Tyminska, L., Stachura, G., and Kolonko, M.: Modelling the Increasing Risk of Damage from Derechos to European Forests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9429, https://doi.org/10.5194/egusphere-egu26-9429, 2026.
Urban risk assessment for natural hazards demands a comprehensive methodology that captures the intricate interdependencies between a city's critical infrastructure and its underlying socio-economic networks. Contextually, it is crucial to incorporate key behavioural mechanisms shaping community resilience and adaptive response to hazardous events. This work proposes a network-based risk model that quantifies the loss of service benefits experienced by users following a flood impact. By combining infrastructure data from various European cities, variations in user flows are modeled through a probabilistic attachment law. This rule describes how users choose services depending on behavioural gains, allowing them to recalculate their socio-economic options and, where possible, adapt by establishing new connections. The findings indicate a critical threshold mechanism: once the hazard intensity exceeds a certain level, it triggers a rapid cascade of disruptions throughout the urban fabric. Nonetheless, this propagation is moderated by adaptive mechanisms, which determine the network's resilience to floods. The proposed framework provides a scalable and transferable tool for assessing and mitigating systemic urban risk, yielding a fine-grained understanding of urban responses to natural hazards and informing resilience strategies aimed at maintaining service continuity.
How to cite: Fidelibus, N., Arosio, M., and Starnini, M.: Beyond direct damage: Cascading disruptions and adaptation in flood-affected socio-economic networks across European cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13395, https://doi.org/10.5194/egusphere-egu26-13395, 2026.
Pesticides are critical agricultural inputs for ensuring food security and may be shaped by climatic changes. Yet, the sensitivity of pesticide use to climate warming at the global scale remains unclear. By integrating country-level pesticide use and high-resolution climate reanalysis data into a fixed-effect panel regression model, we thoroughly investigated how extreme heat affected pesticide use and the heterogeneity of these effects across different levels of economic development. We further projected spatiotemporal trends of global pesticide use under an ensemble of future warming scenarios in a forward-looking manner. Our results can help quantify the impact of climate change on agricultural chemical inputs and provide an essential scientific basis for developing climate-resilient agricultural management strategies.
How to cite: Yang, X., Jiang, S., and Wu, X.: Impacts of extreme heat stress on global pesticide application, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16262, https://doi.org/10.5194/egusphere-egu26-16262, 2026.
The prediction of convective storms, even a few hours in advance, could help reduce the impact of associated phenomena such as heavy rainfall, strong winds, lightning, and large hail. Although highly beneficial to society, accurately forecasting where and when these phenomena will occur remains a major challenge. This is due both to the wide range of spatial scales involved and to the rapid temporal evolution of these events, which typically last from minutes to a few hours. Recent research indicates that the predictability of such events can be significantly improved by incorporating local meteorological observations.
In this context, the ICREN project (Intense Convective Rainfall Events Nowcasting) investigated the possibility of enhancing the nowcasting of convective events in the Seveso River Basin, located in the Lombardy region of Northern Italy, where such events frequently trigger floods and flash floods, severely impacting the urban area of Milan.
The aim of the project was to exploit information provided by local standard and non-conventional meteorological observations through an ad hoc model that integrates physically based Numerical Weather Prediction (NWP) models with data-driven black-box Neural Networks (NNs). The NWP model supports the NN by providing pseudo-observations in the form of forecasted variables, while the fast numerical NN is used to advance the predictions in time and to generate ensemble forecasts of convective phenomena.
This presentation mainly focuses on the research activities devoted to the development of data-driven models and their intercomparison. Furthermore, it illustrates how these models perform with respect to NWP model predictions, both before and after the assimilation of local observations, in order to address the main research question of the project: namely, whether data-driven models are able to integrate NWP predictions at a very local scale and to rapidly advance these predictions in time. In other words, is there an advantage in coupling these two types of models, and to what extent?
Although NN model accuracy decreases with forecast lead time, the predictions outperform those of the NWP models in terms of localization of convective phenomena, confirming that their combination can enhance current NWP forecasting capabilities.
How to cite: Venuti, G., Song, X., Federico, S., Zaf, R. D., Younis, F., Guariso, G., Sangiorgio, M., Pasquero, C., Hassantabar Bozroudi, S. H. H., Luini, L., Nebuloni, R., and Realini, E.: Convective Rainfall Nowcasting: comparison between Numerical Weather Prediction models and Neural Networks in view of an integrated approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20174, https://doi.org/10.5194/egusphere-egu26-20174, 2026.
The identification of regional climate change “hotspots” (areas projected to experience pronounced and impactful changes) is a critical step in prioritizing adaptation resources and policy interventions. The Regional Climate Change Index (RCCI) provides a standardized framework for classifying regional climate risk (low, medium, high) by synthesizing changes in mean precipitation, surface air temperature, and their interannual variability. To move beyond single-model generation assessments and quantify the robustness of these risk classifications, this study introduces a novel Risk Reliability Index (RRI). The RRI is calculated by cross-multiplying RCCI risk classifications (1=low, 2=medium, 3=high) across three generations of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6), summing the products of all unique pairwise combinations for each grid cell. This yields an index ranging from 3 to 27, where higher values indicate not only a higher classified risk but also stronger agreement across model generations, enhancing confidence in the projected regional signal. Analysis of the resulting global Risk Reliability Matrix reveals distinct geographical patterns of multi-generational consensus. The highest RRI values (indicating higher risk with stronger model agreement) are consistently identified in the Mediterranean Basin, Sahara and Sahel regions, Arabian Peninsula, parts of the Amazon region, Northeast and Central Brazil, Southern Africa, and high-latitude Northern Hemisphere regions, including the Arctic and Siberia. These areas emerge as persistent climate hotspots where successive model generations robustly project compounded changes. Conversely, the lowest RRI values (indicating lower risk with stronger model agreement) are found over extensive oceanic regions, particularly the Southern Ocean and parts of the eastern tropical Pacific, southern portion of South America, as well as some continental interiors. While not risk-free, these regions show the most consistent inter-model projection of relatively lower magnitude changes across the three CMIP ensembles. This work underscores that regions with high RRI values represent priority targets for climate adaptation due to both the severity of projected changes and the high confidence across model generations. The Risk Reliability Index provides a simple, transparent metric for integrating multi-model, multi-generational evidence into climate risk assessments, offering a valuable tool for scientists and policymakers to identify regions where climate change signals are most robust and actionable.
How to cite: Torres, R. R.: Multi-Generational CMIP Consensus on Regional Climate Risk, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2002, https://doi.org/10.5194/egusphere-egu26-2002, 2026.
