Evaluation of precipitation forecasting base on GraphCast over mainland China

Evaluation of precipitation forecasting base on GraphCast over mainland China | 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 Evaluation of precipitation forecasting base on GraphCast over mainland China Zihuang Yan, Xianghui Lu, Lifeng Wu, Fa Liu, Rangjian Qiu, Yaokui Cui, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4645037/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The accurate cumulative precipitation forecasts are essential for monitoring water resources and natural disasters. Thecombination of deep learning and big data has become a new direction for precipitation forecasting. However, the currentlarge models are still lacking in-situ data verification. To accomplish this goal, the precipitation forecasting performance of astate-of-the-art model GraphCast was evaluated. Using the cumulative precipitation data from 2393 observation stations for the1-3 day period as a reference, we assessed the cumulative precipitation in mainland China region for the 1-3 day period from2020 to 2021, utilizing a high-resolution model with 0.25◦×0.25◦ grid spacing and 37 layers of parameters. The precipitation ofEuropean Centre for Medium-Range Weather Forecasts (ECMWF) was also compared. The results show that: (1) During the2020-2021 period, for the 1-day, 2-day, and 3-day cumulative precipitation forecasts, the Root Mean Square Error (RMSE)values of GraphCast were primarily between 0.46 to 9.38 mm/d, 0.44 to 9.06 mm/d, and 0.44 to 9.06 mm/d, respectively. TheMean Error (ME) values were mainly between −0.595 to 1.705 (0.01 mm). (2) As the forecast period extends, the forecastingcapability of GraphCast declines. (3) In the 1-3 day cumulative precipitation forecasts for various stations in mainland China,GraphCast demonstrates higher predictive accuracy than ECMWF. (4) Compared to ECMWF, GraphCast demonstrated thebest forecast performance in the warm-temperate humid and sub-humid north China, with the RMSE being approximately 12%higher. Our study indicates that GraphCast demonstrates significant potential and higher accuracy in precipitation forecasting. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Hydrology Cumulative precipitation forecast Large Models High Resolution ECMWF Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Oct, 2024 Reviews received at journal 17 Oct, 2024 Reviews received at journal 07 Oct, 2024 Reviews received at journal 05 Oct, 2024 Reviewers agreed at journal 29 Sep, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers agreed at journal 24 Sep, 2024 Reviewers invited by journal 24 Sep, 2024 Editor assigned by journal 18 Sep, 2024 Editor invited by journal 02 Jul, 2024 Submission checks completed at journal 02 Jul, 2024 First submitted to journal 26 Jun, 2024 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-4645037","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":328662386,"identity":"9333ff86-69ff-4e04-8097-7df128eac328","order_by":0,"name":"Zihuang Yan","email":"","orcid":"","institution":"School of Hydraulic and Ecological Engineering Nanchang Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zihuang","middleName":"","lastName":"Yan","suffix":""},{"id":328662388,"identity":"0e8b2ea3-8696-48b5-bc9d-3344444566d3","order_by":1,"name":"Xianghui 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Thecombination of deep learning and big data has become a new direction for precipitation forecasting. However, the currentlarge models are still lacking in-situ data verification. To accomplish this goal, the precipitation forecasting performance of astate-of-the-art model GraphCast was evaluated. Using the cumulative precipitation data from 2393 observation stations for the1-3 day period as a reference, we assessed the cumulative precipitation in mainland China region for the 1-3 day period from2020 to 2021, utilizing a high-resolution model with 0.25◦×0.25◦ grid spacing and 37 layers of parameters. The precipitation ofEuropean Centre for Medium-Range Weather Forecasts (ECMWF) was also compared. The results show that: (1) During the2020-2021 period, for the 1-day, 2-day, and 3-day cumulative precipitation forecasts, the Root Mean Square Error (RMSE)values of GraphCast were primarily between 0.46 to 9.38 mm/d, 0.44 to 9.06 mm/d, and 0.44 to 9.06 mm/d, respectively. TheMean Error (ME) values were mainly between −0.595 to 1.705 (0.01 mm). (2) As the forecast period extends, the forecastingcapability of GraphCast declines. (3) In the 1-3 day cumulative precipitation forecasts for various stations in mainland China,GraphCast demonstrates higher predictive accuracy than ECMWF. (4) Compared to ECMWF, GraphCast demonstrated thebest forecast performance in the warm-temperate humid and sub-humid north China, with the RMSE being approximately 12%higher. 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