Climate Model Analysis of Extreme Precipitation Clustering and Flood Risk in Europe

Climate Model Analysis of Extreme Precipitation Clustering and Flood Risk in Europe | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate Model Analysis of Extreme Precipitation Clustering and Flood Risk in Europe David R. Miller, Thomas J. Bennett, Laura M. de Vries This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8060703/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Extreme precipitation plays a key role in flood risk management in Europe, where multi-day heavy rainfall often causes severe impacts. This study assessed how well climate models reproduce both the intensity and the temporal distribution of extreme events using daily data from 1979 to 2023. Observations from the E-OBS dataset were compared with outputs from CMIP6 models, with a focus on maximum consecutive wet days and clustering of events. The results show that median spatial correlations between simulated and observed extreme-day counts were about 0.6–0.7, but models often underestimated the duration of wet spells and misrepresented their order, especially in mountainous and coastal areas. Regression analysis and Kling–Gupta Efficiency values pointed to biases related to convection and microphysics schemes. The results indicate that models reproduce large-scale patterns but do not represent storm duration and clustering with enough accuracy. This work shows the need to improve model physics and post-processing methods to better capture the temporal structure of extreme precipitation and to support practical use in flood prediction and water management. Meteorology Atmospheric Sciences Climate Analysis and Modeling Marine and Freshwater Ecology extreme precipitation climate models temporal clustering Europe flood risk hydrological modeling wet spells Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8060703","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541786758,"identity":"cb369d78-cce8-4076-a2fa-a8b69907db8d","order_by":0,"name":"David R. 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