Europe’s extreme temperatures and rainfall in finer detail: strengths and limits of climate model downscaling

Europe’s extreme temperatures and rainfall in finer detail: strengths and limits of climate model downscaling | 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 Article Europe’s extreme temperatures and rainfall in finer detail: strengths and limits of climate model downscaling Mira Hulkkonen, Akash Deshmukh, Tero Mielonen, Ian Brosnan, Taejin Park, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9180873/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Climate extremes in Europe are becoming increasingly frequent and severe, heightening the need for reliable regional climate information to support preparedness and adaptation. Downscaled climate model (DCM) products are widely used for this purpose. Yet, their accuracy relative to global climate models (GCMs) and observations remains uncertain, particularly for extremes. We evaluated multiple DCM ensembles against GCMs and reanalysis datasets, using gridded observational data as reference. Model accuracy was assessed for mean and extreme temperature and precipitation across Europe using complementary metrics that quantify distributional agreement, pointwise accuracy, and bias magnitude and direction. The results show that downscaling generally improves the representation of European climate. Statistically downscaled and bias-corrected products outperform GCMs for mean and extreme temperature, and for mean precipitation, whereas gains for extreme precipitation are limited. The dynamically downscaled ensemble exhibits systematic regional biases, notably for temperature in the Nordics. No single dataset performs best across all regions. Across models, accuracy is reduced in areas with complex terrain (mountains and coastlines). Observational uncertainty further complicates bias assessment. Our findings highlight both the value and limitations of climate model downscaling: while DCMs provide critical fine-scale information, their reliability is variable- and region-dependent, with extreme precipitation remaining particularly difficult to capture. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards climate change climate model model downscaling climate extremes Full Text Additional Declarations No competing interests reported. Supplementary Files SIHulkkonenetal.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 07 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 20 Mar, 2026 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. 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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-9180873","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611326362,"identity":"c3da4341-6957-4738-83f4-d60d9f89e3c2","order_by":0,"name":"Mira Hulkkonen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACZiBmbGCQkeABc234QSIfEojQwgPVkibZwMzAOAOvFgZULYclG4D8GfhU67bzPvzAuOMwj2TP4WMPPu44L6HbDjTiAR4tZofZjSUYzxzmkeZtSzeceea2hNlhoBZ8DjM7zMYgwdh2mEeOn8cMqO12HVAL+wMCWph/wLX8bTtHlC1sYFukeXvMpBnbDhCnxSKxLR3o/WNpkr1tyUAtjI34tZw/xnzjY5u1nMSZ5GMSP9vsJMzOHz7Y+AOPFjBAMxMYTaNgFIyCUTAKKAMAsC9HkkTPy5EAAAAASUVORK5CYII=","orcid":"","institution":"Finnish Meteorological Institute","correspondingAuthor":true,"prefix":"","firstName":"Mira","middleName":"","lastName":"Hulkkonen","suffix":""},{"id":611326363,"identity":"46b47fd8-dff8-4d25-ba6d-1ef10032a9e6","order_by":1,"name":"Akash Deshmukh","email":"","orcid":"","institution":"Finnish Meteorological Institute","correspondingAuthor":false,"prefix":"","firstName":"Akash","middleName":"","lastName":"Deshmukh","suffix":""},{"id":611326364,"identity":"5638de53-346b-4938-9171-a35cbc348bfe","order_by":2,"name":"Tero Mielonen","email":"","orcid":"","institution":"Finnish Meteorological Institute","correspondingAuthor":false,"prefix":"","firstName":"Tero","middleName":"","lastName":"Mielonen","suffix":""},{"id":611326365,"identity":"8b63340f-3208-42b1-820e-64e0d6a38f5d","order_by":3,"name":"Ian Brosnan","email":"","orcid":"","institution":"NASA Ames Research Center","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Brosnan","suffix":""},{"id":611326366,"identity":"18de7e9a-b6f4-46c0-b17e-a6a10b24b30c","order_by":4,"name":"Taejin Park","email":"","orcid":"","institution":"NASA Ames Research Center","correspondingAuthor":false,"prefix":"","firstName":"Taejin","middleName":"","lastName":"Park","suffix":""},{"id":611326369,"identity":"ac170a0a-b1f7-4f76-bbdf-3e114da3eca0","order_by":5,"name":"Hugo Lee","email":"","orcid":"","institution":"Jet Propulsion Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Hugo","middleName":"","lastName":"Lee","suffix":""},{"id":611326370,"identity":"dbbdff7d-76b8-40f6-8015-3bafa2c05d1f","order_by":6,"name":"Weile Wang","email":"","orcid":"","institution":"NASA Ames Research Center","correspondingAuthor":false,"prefix":"","firstName":"Weile","middleName":"","lastName":"Wang","suffix":""},{"id":611326371,"identity":"6243ccad-79fd-4bf0-92a9-3a6968607d3e","order_by":7,"name":"Bridget Thrasher","email":"","orcid":"","institution":"NASA Ames Research Center","correspondingAuthor":false,"prefix":"","firstName":"Bridget","middleName":"","lastName":"Thrasher","suffix":""},{"id":611326372,"identity":"357a7282-f74e-436a-be35-f0d800a12437","order_by":8,"name":"Jessica L. 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Downscaled climate model (DCM) products are widely used for this purpose. Yet, their accuracy relative to global climate models (GCMs) and observations remains uncertain, particularly for extremes. We evaluated multiple DCM ensembles against GCMs and reanalysis datasets, using gridded observational data as reference. Model accuracy was assessed for mean and extreme temperature and precipitation across Europe using complementary metrics that quantify distributional agreement, pointwise accuracy, and bias magnitude and direction.\n\nThe results show that downscaling generally improves the representation of European climate. Statistically downscaled and bias-corrected products outperform GCMs for mean and extreme temperature, and for mean precipitation, whereas gains for extreme precipitation are limited. The dynamically downscaled ensemble exhibits systematic regional biases, notably for temperature in the Nordics. No single dataset performs best across all regions. 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