Enhancing the Robustness of Temperature Simulations in India through a Bias-Corrected Multi-Model Ensemble Framework

Enhancing the Robustness of Temperature Simulations in India through a Bias-Corrected Multi-Model Ensemble Framework | 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 Short Report Enhancing the Robustness of Temperature Simulations in India through a Bias-Corrected Multi-Model Ensemble Framework Avijit Paul, Monomoy Goswami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9094575/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Reliable regional-scale temperature projections are essential for climate impact assessment and strategizing remedial measures for future. The complex physiography, which is the characteristics of mainland India, introduces large uncertainties in Global Climate Model (GCM) outputs for temperature analysis over the vast expanse of this geographical entity. This study presents a framework for reducing the uncertainties of GCM outputs by applying a multi-model ensemble (MME) technique using CMIP6 Bias Corrected GCM outputs. Monthly maximum, mean, and minimum temperatures for the period 1965–2014 were evaluated against ERA5 reanalysis data. Five Multi-Model Ensemble (MME) techniques—Equal Weight Averaging, Bates–Granger Averaging, Granger–Ramanathan Averaging (GRA), Mallows Model Averaging, and Bayesian Model Averaging—were systematically assessed using k-fold cross-validation and full-sample calibration at multiple spatial resolutions. Results show that GRA consistently outperforms other techniques by minimizing mean absolute error and maximizing the index of agreement across all temperature variables. The proposed framework enhances the robustness of temperature simulations and provides a strong foundation for future climate projections under different emission scenarios. CMIP6 Multi-model ensemble Temperature variability Granger–Ramanathan Averaging India Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 11 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 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. 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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-9094575","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":628644280,"identity":"33ed1984-e32c-4093-99eb-62bfaaa94400","order_by":0,"name":"Avijit Paul","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBADHsZmCEOGH0QmFBClhRnCkGwAaTEgyiKoFoMDIAqPFn7p9msfGGoOyzC38x9gLqg5zGN8fnXihwcGDPL8YgewapGcc6Z4BsOxw2CHMc8AMsxuvN0sAXSY4czZCVi1GNzISWZgYEuDaOFhA2k5uwGkJcHgNnYt9mAt/2Ba/gEdNuPs5h/4tBhIpB9mYGyzgWjhbTvMY8Dfuw2vLRI3cpgZEvvAWgwOz+xL55G4wbvNIsFAAqdf+GekP2b48E3C3rD/4MPHBd+s5fj7z26++aPCRp5fGrsWUEQwgKQMGxgYDkMsBquUwKEcBNgfgCl5Blhk8h/Ao3oUjIJRMApGIgAAKINVms6tTPkAAAAASUVORK5CYII=","orcid":"","institution":"Central Institute of Technology Kokrajhar","correspondingAuthor":true,"prefix":"","firstName":"Avijit","middleName":"","lastName":"Paul","suffix":""},{"id":628644281,"identity":"007f1560-46aa-43d7-890b-d3d6fcc981fc","order_by":1,"name":"Monomoy Goswami","email":"","orcid":"","institution":"Central Institute of Technology Kokrajhar","correspondingAuthor":false,"prefix":"","firstName":"Monomoy","middleName":"","lastName":"Goswami","suffix":""}],"badges":[],"createdAt":"2026-03-11 12:58:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9094575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9094575/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182652,"identity":"96ac3d9a-be44-428a-85ee-7b7219a7fe0b","added_by":"auto","created_at":"2026-04-30 08:59:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1649110,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancingtheRobustnessofTemperatureSimulationsinIndiathroughaBiasCorrectedMultiModelEnsembleFramework.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9094575/v1_covered_66f69b64-56f2-495d-b5e2-c682cd57c7f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEnhancing the Robustness of Temperature Simulations in India through a Bias-Corrected Multi-Model Ensemble Framework\u003c/p\u003e","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, Multi-model ensemble, Temperature variability, Granger–Ramanathan Averaging, India","lastPublishedDoi":"10.21203/rs.3.rs-9094575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9094575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReliable regional-scale temperature projections are essential for climate impact assessment and strategizing remedial measures for future. The complex physiography, which is the characteristics of mainland India, introduces large uncertainties in Global Climate Model (GCM) outputs for temperature analysis over the vast expanse of this geographical entity. This study presents a framework for reducing the uncertainties of GCM outputs by applying a multi-model ensemble (MME) technique using CMIP6 Bias Corrected GCM outputs. Monthly maximum, mean, and minimum temperatures for the period 1965\u0026ndash;2014 were evaluated against ERA5 reanalysis data. Five Multi-Model Ensemble (MME) techniques\u0026mdash;Equal Weight Averaging, Bates\u0026ndash;Granger Averaging, Granger\u0026ndash;Ramanathan Averaging (GRA), Mallows Model Averaging, and Bayesian Model Averaging\u0026mdash;were systematically assessed using k-fold cross-validation and full-sample calibration at multiple spatial resolutions. Results show that GRA consistently outperforms other techniques by minimizing mean absolute error and maximizing the index of agreement across all temperature variables. 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