Out-of-Sample Statistical Correctability Limits under an Uncertain Operational Reference: The Case of IMERG Sub-daily Areal Precipitation Extremes

Out-of-Sample Statistical Correctability Limits under an Uncertain Operational Reference: The Case of IMERG Sub-daily Areal Precipitation 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 Research Article Out-of-Sample Statistical Correctability Limits under an Uncertain Operational Reference: The Case of IMERG Sub-daily Areal Precipitation Extremes Marc Semper, Manuel Curado, Jose F. Vicent, Leandro Tortosa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9185232/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract We present a statistical assessment of out-of-sample correctability limits under observational uncertainty, using GPM IMERG Final V07 sub-daily areal precipitation extremes over the Comunitat Valenciana (eastern Spain) and the dense AVAMET network (556 gauges, 2019--2025). Rather than treating gauges as point truth, we construct a gauge-derived multi-station operational areal reference proxy at 30 min over 126 IMERG cells and build a closed benchmark at 1, 3, and 6 h under year-holdout, province-holdout, and event-fold validation. The benchmark compares raw IMERG (M0), two simple statistical corrections (M1--M2), a continuous LightGBM corrector (M3), a two-stage tail-oriented LightGBM model (M3b), and a conservative patch-based CNN specialist (M4). We further evaluate reference sensitivity under alternative proxy definitions and pseudo leave-one-gauge-out perturbations, and we treat direct probabilistic exceedance modelling as a core benchmark component alongside deterministic correction. Under the main operational-median proxy, M3 is the best global continuous corrector at all scales, reducing event-fold RMSE from 0.4606 to 0.3597 at 1 h, from 0.8013 to 0.5783 at 3 h, and from 1.4452 to 1.2587 at 6 h. However, these gains do not translate into clean recovery of severe and extreme events under fixed operational thresholds. Tail-oriented models improve severe-event skill relative to M3 at 3 h and 6 h, but only modestly, while fixed extreme recovery remains weak across reference perturbations and hard holdouts. The conservative local deep-learning specialist does not materially improve the tail-oriented tabular baseline. About half of the severe and extreme cases occur in cells with the minimum observational support of two gauges, and local jackknife diagnostics show increasing proxy sensitivity in the upper tail. Peak and alignment-oracle diagnostics indicate that residual error is only partly explained by local spatiotemporal misalignment; substantial amplitude underestimation persists even after temporal and neighborhood tolerance, and IMERG often enters far below the operational threshold in observed extreme cases. Importantly, direct probabilistic exceedance modelling emerges as a main positive result: it provides robust out-of-sample risk ranking for severe and extreme exceedance across split families, although operational precision remains limited by event rarity. Overall, the results support partial out-of-sample correctability of IMERG sub-daily areal extremes under an operational areal proxy, while probabilistic risk ranking emerges as the central operational output when clean deterministic recovery of the fixed extreme tail remains unattained. GPM IMERG sub-daily precipitation extremes operational areal proxy out-of-sample correctability limits proxy sensitivity probabilistic exceedance risk Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 May, 2026 Reviews received at journal 30 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 21 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. 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-9185232","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612078350,"identity":"209e0719-0a66-4a6e-bb1e-bd32d3cbffe5","order_by":0,"name":"Marc Semper","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYJADxgcka2E2IFkLmwRRynTbzz78wPDHJtqcvfdZNU8Ng5x8AwEtZmfSjSUY29Jyd/YcN7vNc4zB2OAAIS0H0tgYGBsO5264kcZ2m4eNIXEDIYeZnX/GxsDw53/uhvvP2Ip5/jHUzyfoMKDhQI8fANrCxsbM28aQwEDQYTeeMUsktiUD/ZLGLDm3T8JwA0Et59MYP3z4Y5e7nf0Y44c332zkCYYYGCQAMTQaiYsaCCA95kfBKBgFo2DEAABfAzwhZETpAgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Alicante","correspondingAuthor":true,"prefix":"","firstName":"Marc","middleName":"","lastName":"Semper","suffix":""},{"id":612078351,"identity":"abf87d57-5c0c-441d-b7a7-837837a475e9","order_by":1,"name":"Manuel Curado","email":"","orcid":"","institution":"University of Alicante","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Curado","suffix":""},{"id":612078352,"identity":"aefb5438-4b47-4724-9210-388b20f73482","order_by":2,"name":"Jose F. Vicent","email":"","orcid":"","institution":"University of Alicante","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"F.","lastName":"Vicent","suffix":""},{"id":612078353,"identity":"48539416-16c9-41dd-bd8d-e6abc70ec32d","order_by":3,"name":"Leandro Tortosa","email":"","orcid":"","institution":"University of Alicante","correspondingAuthor":false,"prefix":"","firstName":"Leandro","middleName":"","lastName":"Tortosa","suffix":""}],"badges":[],"createdAt":"2026-03-21 10:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9185232/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9185232/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105567102,"identity":"ade0c7b3-b875-426e-a8ed-21d32f3d206c","added_by":"auto","created_at":"2026-03-27 12:58:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2321352,"visible":true,"origin":"","legend":"","description":"","filename":"OutofSampleCorrectabilityofIMERG.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9185232/v1_covered_7b17d0e6-417f-44c9-9e62-42ff9619b5bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Out-of-Sample Statistical Correctability Limits under an Uncertain Operational Reference: The Case of IMERG Sub-daily Areal Precipitation Extremes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"stochastic-environmental-research-and-risk-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"serr","sideBox":"Learn more about [Stochastic Environmental Research and Risk Assessment](https://www.springer.com/journal/477)","snPcode":"477","submissionUrl":"https://submission.nature.com/new-submission/477/3","title":"Stochastic Environmental Research and Risk Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"GPM IMERG, sub-daily precipitation extremes, operational areal proxy, out-of-sample correctability limits, proxy sensitivity, probabilistic exceedance risk","lastPublishedDoi":"10.21203/rs.3.rs-9185232/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9185232/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe present a statistical assessment of out-of-sample correctability limits under observational uncertainty, using GPM IMERG Final V07 sub-daily areal precipitation extremes over the Comunitat Valenciana (eastern Spain) and the dense AVAMET network (556 gauges, 2019--2025). Rather than treating gauges as point truth, we construct a gauge-derived multi-station operational areal reference proxy at 30 min over 126 IMERG cells and build a closed benchmark at 1, 3, and 6 h under year-holdout, province-holdout, and event-fold validation. The benchmark compares raw IMERG (M0), two simple statistical corrections (M1--M2), a continuous LightGBM corrector (M3), a two-stage tail-oriented LightGBM model (M3b), and a conservative patch-based CNN specialist (M4). We further evaluate reference sensitivity under alternative proxy definitions and pseudo leave-one-gauge-out perturbations, and we treat direct probabilistic exceedance modelling as a core benchmark component alongside deterministic correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnder the main operational-median proxy, M3 is the best global continuous corrector at all scales, reducing event-fold RMSE from 0.4606 to 0.3597 at 1 h, from 0.8013 to 0.5783 at 3 h, and from 1.4452 to 1.2587 at 6 h. However, these gains do not translate into clean recovery of severe and extreme events under fixed operational thresholds. Tail-oriented models improve severe-event skill relative to M3 at 3 h and 6 h, but only modestly, while fixed extreme recovery remains weak across reference perturbations and hard holdouts. The conservative local deep-learning specialist does not materially improve the tail-oriented tabular baseline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbout half of the severe and extreme cases occur in cells with the minimum observational support of two gauges, and local jackknife diagnostics show increasing proxy sensitivity in the upper tail. Peak and alignment-oracle diagnostics indicate that residual error is only partly explained by local spatiotemporal misalignment; substantial amplitude underestimation persists even after temporal and neighborhood tolerance, and IMERG often enters far below the operational threshold in observed extreme cases. Importantly, direct probabilistic exceedance modelling emerges as a main positive result: it provides robust out-of-sample risk ranking for severe and extreme exceedance across split families, although operational precision remains limited by event rarity. Overall, the results support partial out-of-sample correctability of IMERG sub-daily areal extremes under an operational areal proxy, while probabilistic risk ranking emerges as the central operational output when clean deterministic recovery of the fixed extreme tail remains unattained.\u003c/p\u003e","manuscriptTitle":"Out-of-Sample Statistical Correctability Limits under an Uncertain Operational Reference: The Case of IMERG Sub-daily Areal Precipitation Extremes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 22:18:06","doi":"10.21203/rs.3.rs-9185232/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-06T23:09:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T14:18:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T14:50:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310683357554755838467588074773776438246","date":"2026-04-02T05:09:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135631164119218977599433970899889428846","date":"2026-03-25T14:01:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-24T23:23:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T22:41:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T08:38:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Stochastic Environmental Research and Risk Assessment","date":"2026-03-21T10:43:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"stochastic-environmental-research-and-risk-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"serr","sideBox":"Learn more about [Stochastic Environmental Research and Risk Assessment](https://www.springer.com/journal/477)","snPcode":"477","submissionUrl":"https://submission.nature.com/new-submission/477/3","title":"Stochastic Environmental Research and Risk Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"305751eb-6bd9-40c2-9cb1-13bf22728587","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-06T23:09:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T14:18:14+00:00","index":28,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 22:18:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9185232","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9185232","identity":"rs-9185232","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}