Adapting the EcPoint Post-Processing methodology for ensemble rainfall forecasts to CMA-GEPS numerical model, a Regional Calibration over the Yangtze River basin in eastern China

Adapting the EcPoint Post-Processing methodology for ensemble rainfall forecasts to CMA-GEPS numerical model, a Regional Calibration over the Yangtze River basin in eastern 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 Research Article Adapting the EcPoint Post-Processing methodology for ensemble rainfall forecasts to CMA-GEPS numerical model, a Regional Calibration over the Yangtze River basin in eastern China Teddy Mwira, Sonum Stejik, Moses David Tumusiime, Samuel Ekwacu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8240684/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 Accurate probabilistic rainfall forecasting remains a major challenge, particularly in regions characterized by complex topography and high climatic variability. Small errors arising from observations, data assimilation, and model configuration can grow nonlinearly, leading to rapid loss of forecast skill with time. Traditionally, rainfall forecasts have been generated at grid-based spatial resolutions however, Weather varies markedly within a grid box and therefore grid-based ensemble forecasts often struggle to capture sub-grid variability and localized extremes, limiting their usefulness for operational forecasting at specific points of interest. The EcPoint post-processing approach, developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), addresses this limitation by tailoring ensemble forecasts to point-specific locations. This study adapts the approach to the China Meteorological Administration Global Ensemble Prediction System (CMA-GEPS) through regional calibration over the Yangtze River Basin covering provinces of Anhui, Jiangsu, and Zhejiang. The performance of the GEPS EcPoint was compared with both the Raw GEPS and the original ECMWF EcPoint system using probabilistic verification metrics. A case study of the 30 July 2022 Xuzhou flooding event was also studied to assess spatial rainfall representation. The study highlighted the added value of the post-processing method, five months of verification demonstrated that between the Raw ensemble and post-processed forecasts, EcPoint is more reliable and skillful markedly improving forecast performance across all rainfall thresholds and lead times, with significant reductions in CRPS and higher AUC values relative to the Raw ensemble. The Xuzhou case study further illustrated EcPoint’s ability to capture localized heavy rainfall with greater spatial accuracy and reducing false alarms compared to the Raw ensemble. EcPoint reasonably forecasts the spatial coverages of the rainfall event while the Raw GEPS presents a notable overestimation. The ECMWF EcPoint, which incorporates a broader range of predictors such as solar radiation, exhibited the higher skill and a stronger diurnal cycle. Nonetheless, the GEPS EcPoint demonstrated substantial value-added skill, confirming the robustness and transferability of the EcPoint methodology. Overall, the findings highlight EcPoint’s effectiveness as a model-independent, data-driven tool for enhancing ensemble rainfall forecasts. Its demonstrated adaptability across different ensemble systems underscores its potential for operational adoption, offering improved reliability for flood risk management and early warning applications. Probabilistic Ensemble forecasts Post-processing EcPoint methodology GEPS Flood risk management Full Text Additional Declarations No competing interests reported. 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-8240684","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555281654,"identity":"61cfe3fb-fe34-40b2-8fa1-8c8fd9094155","order_by":0,"name":"Teddy 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China","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Probabilistic Ensemble forecasts, Post-processing, EcPoint methodology, GEPS, Flood risk management","lastPublishedDoi":"10.21203/rs.3.rs-8240684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8240684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate probabilistic rainfall forecasting remains a major challenge, particularly in regions characterized by complex topography and high climatic variability. Small errors arising from observations, data assimilation, and model configuration can grow nonlinearly, leading to rapid loss of forecast skill with time. Traditionally, rainfall forecasts have been generated at grid-based spatial resolutions however, Weather varies markedly within a grid box and therefore grid-based ensemble forecasts often struggle to capture sub-grid variability and localized extremes, limiting their usefulness for operational forecasting at specific points of interest. The EcPoint post-processing approach, developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), addresses this limitation by tailoring ensemble forecasts to point-specific locations. This study adapts the approach to the China Meteorological Administration Global Ensemble Prediction System (CMA-GEPS) through regional calibration over the Yangtze River Basin covering provinces of Anhui, Jiangsu, and Zhejiang. The performance of the GEPS EcPoint was compared with both the Raw GEPS and the original ECMWF EcPoint system using probabilistic verification metrics. A case study of the 30 July 2022 Xuzhou flooding event was also studied to assess spatial rainfall representation.\u003c/p\u003e\u003cp\u003eThe study highlighted the added value of the post-processing method, five months of verification demonstrated that between the Raw ensemble and post-processed forecasts, EcPoint is more reliable and skillful markedly improving forecast performance across all rainfall thresholds and lead times, with significant reductions in CRPS and higher AUC values relative to the Raw ensemble. The Xuzhou case study further illustrated EcPoint\u0026rsquo;s ability to capture localized heavy rainfall with greater spatial accuracy and reducing false alarms compared to the Raw ensemble. EcPoint reasonably forecasts the spatial coverages of the rainfall event while the Raw GEPS presents a notable overestimation. The ECMWF EcPoint, which incorporates a broader range of predictors such as solar radiation, exhibited the higher skill and a stronger diurnal cycle. Nonetheless, the GEPS EcPoint demonstrated substantial value-added skill, confirming the robustness and transferability of the EcPoint methodology. Overall, the findings highlight EcPoint\u0026rsquo;s effectiveness as a model-independent, data-driven tool for enhancing ensemble rainfall forecasts. Its demonstrated adaptability across different ensemble systems underscores its potential for operational adoption, offering improved reliability for flood risk management and early warning applications.\u003c/p\u003e","manuscriptTitle":"Adapting the EcPoint Post-Processing methodology for ensemble rainfall forecasts to CMA-GEPS numerical model, a Regional Calibration over the Yangtze River basin in eastern China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 07:10:18","doi":"10.21203/rs.3.rs-8240684/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"809db461-4a6f-4dee-a180-f4ca8fbdda03","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-01T23:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 07:10:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8240684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8240684","identity":"rs-8240684","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}