Cost-Sensitive Deep Learning Framework for Predicting Rare Extreme Weather Events

Cost-Sensitive Deep Learning Framework for Predicting Rare Extreme Weather Events | 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 Cost-Sensitive Deep Learning Framework for Predicting Rare Extreme Weather Events Abdullah Fadhil Tawfeeq, Ümit Haluk Atasever This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9123454/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 Extreme weather events such as heatwaves and coldwaves represent major climate hazards that pose increasing risks to human health, water and energy systems, agriculture, and infrastructure. Accurate prediction of these rare events is therefore critical for improving environmental risk assessment and early warning systems. In this study, we develop a deep learning–based framework for predicting heatwaves and coldwaves using daily meteorological observations from five ground weather stations. Nine deep learning architectures, including recurrent, graph-based, spatio-temporal, and attention-based neural networks, are evaluated across multiple forecasting horizons. Predicting extreme temperature events presents substantial challenges due to the severe class imbalance between rare extreme events and normal climate conditions, as well as their multi-day duration. To address these challenges, we implement an optimized cost-sensitive learning strategy that prioritizes minority extreme-event classes during model training, together with a spell-based evaluation framework designed for extended climate events. Experimental results show that the spatio-temporal graph convolutional network with gated recurrent units (STGCN-GRU) and the temporal convolutional network (TCN) achieve the best predictive performance in terms of F1 score across forecasting horizons. Ablation experiments further demonstrate that the proposed cost-sensitive learning strategy provides more effective handling of class imbalance than conventional resampling approaches. The proposed framework offers a promising data-driven approach for improving the prediction and risk assessment of rare temperature extremes. Heatwave prediction coldwave prediction class imbalance spell-based evaluation spatio-temporal deep learning 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-9123454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622802570,"identity":"38961d66-0401-46bd-9c30-31d64cc39fd4","order_by":0,"name":"Abdullah Fadhil Tawfeeq","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACCQYDhgcFDAkMzAeA7AIbkAgRWhKAiIEtAcROI13LYcJa+Gc3b5NIMLDLY2Bjfnjjg8H5xP7ZzQcfMNTYROO05M6xMqCW5GIGNjZjyxkGtxNn3DmWbMBwLC23AZeeGzlmQC3MiQ3yDWbSPEAtDSARxobDOLXIQ7TUJzawsX+T/mNwLnE+IS0GEC2HgVp4zKQZDA4kbiCkxfDOsWKLBIPjiW1sPMWWPQbJxhtvpCUbJODxi9zt5o03PlRUJ/azsW+88aPCTnbejeSDDz7U2OD2PgywQWlHsMoEQsqRgT0pikfBKBgFo2BkAAAF4ls2gzd5HwAAAABJRU5ErkJggg==","orcid":"","institution":"Erciyes University","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"Fadhil","lastName":"Tawfeeq","suffix":""},{"id":622802573,"identity":"55ec085b-5f64-4e40-8412-4919cf5c9eb0","order_by":1,"name":"Ümit Haluk Atasever","email":"","orcid":"","institution":"Erciyes University","correspondingAuthor":false,"prefix":"","firstName":"Ümit","middleName":"Haluk","lastName":"Atasever","suffix":""}],"badges":[],"createdAt":"2026-03-14 14:53:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9123454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9123454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107710354,"identity":"b2b4a79d-baa1-4979-b7f1-c3152fbfdf03","added_by":"auto","created_at":"2026-04-24 09:40:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":595735,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9123454/v1_covered_0be118af-c731-42f5-a555-4a0e04b7b40a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCost-Sensitive Deep Learning Framework for Predicting Rare Extreme Weather Events\u003c/p\u003e","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":"Heatwave prediction, coldwave prediction, class imbalance, spell-based evaluation, spatio-temporal deep learning","lastPublishedDoi":"10.21203/rs.3.rs-9123454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9123454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExtreme weather events such as heatwaves and coldwaves represent major climate hazards that pose increasing risks to human health, water and energy systems, agriculture, and infrastructure. 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