Global gridded crop models underestimate yield losses from climatic extremes

Global gridded crop models underestimate yield losses from climatic extremes | 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 Global gridded crop models underestimate yield losses from climatic extremes Cornelia Auer, Kobe De Maeyer, Christoph Müller, Michael Höhle, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7181139/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Global gridded crop models (GGCMs) are crucial for projecting the impacts of climate change, yet their performance under climatic extremes remains poorly understood. Using historical subnational yield data, we evaluate 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) across major crops. Models largely capture the signal of heat and drought extremes but consistently underestimate the magnitude of losses. For extreme wet conditions, they often miss both the signal and magnitude (losses underestimated in 83–95% of cases, best to worst model). However, underestimation is reduced for compound heat–drought extremes (38–77%). Notably, the model ensemble median, widely used to reduce uncertainty, performs poorly under extremes, systematically underestimating risks. We identify model characteristics that influence underestimation under extremes, such as the representation of radiation or soil nutrient dynamics. Improving key process representations and input data quality is essential to strengthen climate risk assessments for global agriculture and food security. Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTable1.xlsx Supplementary Table 1 Appendix.pdf Supplementary Information to Cite Share Download PDF Status: Under Review 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. 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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-7181139","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":492419062,"identity":"1e470ab2-edd1-4c89-9d3b-89e2d96ca021","order_by":0,"name":"Cornelia Auer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYNCCAgk4k0eCvQFIGVgQ0GKArIXnAKoIDi1IbAmJBDCFUzF/e++zBx8MLOQZ2M8YPq5guCMjOfP51Q0/gE7lb+9OwKZF4sxxc8MZBhKGDTw5xoZnGJ7xSEvnlN3sATpM4szZDdh9kcYmzWMgwdjAkGMm2cBwmEdOOiftBlAEKJWLW8sfAwn7Bv435j/BWiTPpN38Q0gLkExskMgxYwRpkZZgP3Ybny0SZ46xSQJdntwm8axYssHgGY9kTw7bbRkDCR5cfuFvb2OT+FFRZ9vPn7zxY0PFHXuJ48ef3Xzzx0YOGJhYtcABG8SdB4AEDziaePAqRwIgLewPiFU9CkbBKBgFIwMAAKpGVTStq6KWAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5925-832X","institution":"Potsdam Institute for Climate Impact Research","correspondingAuthor":true,"prefix":"","firstName":"Cornelia","middleName":"","lastName":"Auer","suffix":""},{"id":492419063,"identity":"ef71a252-73c7-4616-ae95-1ab2ca11f5ad","order_by":1,"name":"Kobe De Maeyer","email":"","orcid":"","institution":"Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"Kobe","middleName":"","lastName":"De Maeyer","suffix":""},{"id":492419064,"identity":"8a675681-2ec7-475b-9556-d5f67008fe25","order_by":2,"name":"Christoph Müller","email":"","orcid":"https://orcid.org/0000-0002-9491-3550","institution":"Potsdam Institute for Climate Impact Research","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"","lastName":"Müller","suffix":""},{"id":492419065,"identity":"2c727185-77d5-4c13-be6e-d8e54722cfba","order_by":3,"name":"Michael Höhle","email":"","orcid":"","institution":"University of Greifswald,","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Höhle","suffix":""},{"id":492419066,"identity":"18968cbe-cd08-4338-aa9e-ff680f5fa633","order_by":4,"name":"Jacob Schewe","email":"","orcid":"https://orcid.org/0000-0001-9455-4159","institution":"Potsdam Institute for Climate Impact Research","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Schewe","suffix":""},{"id":492419067,"identity":"84f010c5-52f3-4fe2-96f6-51f59ed71dee","order_by":5,"name":"Jonas Jaegermeyr","email":"","orcid":"https://orcid.org/0000-0002-8368-0018","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Jonas","middleName":"","lastName":"Jaegermeyr","suffix":""},{"id":492419068,"identity":"3e499c7a-1e45-436d-8b60-e874d849d120","order_by":6,"name":"Juraj Balkovic","email":"","orcid":"https://orcid.org/0000-0003-2955-4931","institution":"IIASA - 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Using historical subnational yield data, we evaluate 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) across major crops. Models largely capture the signal of heat and drought extremes but consistently underestimate the magnitude of losses. For extreme wet conditions, they often miss both the signal and magnitude (losses underestimated in 83–95\\% of cases, best to worst model). However, underestimation is reduced for compound heat–drought extremes (38–77\\%). \r\nNotably, the model ensemble median, widely used to reduce uncertainty, performs poorly under extremes, systematically underestimating risks.\r\n We identify model characteristics that influence underestimation under extremes, such as the representation of radiation or soil nutrient dynamics. 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