A Robustness-Based Framework for CMIP6 Precipitation Model Selection Under Multi-Regime and Weight Uncertainty

A Robustness-Based Framework for CMIP6 Precipitation Model Selection Under Multi-Regime and Weight Uncertainty | 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 A Robustness-Based Framework for CMIP6 Precipitation Model Selection Under Multi-Regime and Weight Uncertainty NGUYEN THI HUONG, VO QUANG TUONG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9368849/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Climate model selection for hydrological and climate-impact studies is often conducted under fixed diagnostic configurations and subjective weighting schemes, implicitly assuming stable model rankings. This study presents an uncertainty-aware framework for CMIP6 model selection using monthly precipitation over South Korea as an application case. First, models are evaluated across multiple precipitation regimes representing mean annual precipitation (MAP), monsoon-season precipitation (MMP), upper-tail extremes (P90 and P95, corresponding to the 90th and 95th percentiles), and wet-frequency behavior (WET) using a complementary multi-metric suite. Second, regime-wise ranks are aggregated into a weighted robustness score. Third, uncertainty in evaluation priorities is propagated through Monte Carlo perturbations of diagnostic weights. Finally, model selection is formulated as a risk-adjusted objective that balances central robustness against sensitivity to weight variation. The results indicate pronounced regime dependence in deterministic rankings, with weak agreement between some mean and seasonal diagnostics but stronger consistency within extreme-regime comparisons. Weight-uncertainty analysis further reveals that models with similar central performance can differ substantially in stability. Based on the combined performance-stability trade-off, a compact subset of eight CMIP6 models was identified as the most robust for precipitation-focused applications. The proposed framework provides a systematic framework to move beyond fixed-weight deterministic ranking and can support more defensible ensemble reduction in climate-impact studies. Although demonstrated for precipitation over South Korea, the framework is conceptually transferable to other regions and climate variables, but further validation across different climatic settings is still required. CMIP6 precipitation multi-regime diagnostics model selection uncertainty analysis Monte Carlo perturbation ensemble reduction Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 09 Apr, 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-9368849","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627013283,"identity":"00b4c5d4-57a9-42aa-a68e-608a162a3099","order_by":0,"name":"NGUYEN THI HUONG","email":"","orcid":"","institution":"Nguyen Tat Thanh University","correspondingAuthor":false,"prefix":"","firstName":"NGUYEN","middleName":"THI","lastName":"HUONG","suffix":""},{"id":627013284,"identity":"97371bc6-2548-4295-aa2f-6e6a6dca1b8a","order_by":1,"name":"VO QUANG TUONG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDADfjDJRoRKHhhDsoFkLQYHiNViL5F87OEXhsOym4/3GDB8KDvMYHC7gYAtEmnpxjIMh423nTljwDjjHFDLnQOEtOSYSUswpCVuu5FjwMzbdphBckYCkVo2zwBq+UusFskPDDaJGySAWhiBWvglCGk58yxNmsHAxnjGmWMFB3vOpfMQ1MLennxM8keFhGx/e/PGBz/KrOXYCGkBAWYeAwbGBiDjAANSROEFjD8YIFpGwSgYBaNgFGAFAGwwO/xvpFQpAAAAAElFTkSuQmCC","orcid":"","institution":"Ho Chi Minh City Open University","correspondingAuthor":true,"prefix":"","firstName":"VO","middleName":"QUANG","lastName":"TUONG","suffix":""}],"badges":[],"createdAt":"2026-04-09 12:53:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9368849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9368849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107868255,"identity":"328565af-0794-4d26-b8a8-da1758059317","added_by":"auto","created_at":"2026-04-27 07:09:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":982250,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPTSCMIP6V2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9368849/v1_covered_2a611a66-93a0-45ec-a8a1-7d1f7ca14f34.pdf"},{"id":107704369,"identity":"bda2c0ef-d764-4747-90c2-7250e25d9834","added_by":"auto","created_at":"2026-04-24 08:44:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18572,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9368849/v1/4e4baede8845fb277cec3e4a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Robustness-Based Framework for CMIP6 Precipitation Model Selection Under Multi-Regime and Weight Uncertainty","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":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CMIP6 precipitation, multi-regime diagnostics, model selection, uncertainty analysis, Monte Carlo perturbation, ensemble reduction","lastPublishedDoi":"10.21203/rs.3.rs-9368849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9368849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate model selection for hydrological and climate-impact studies is often conducted under fixed diagnostic configurations and subjective weighting schemes, implicitly assuming stable model rankings. 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