Structural time series analysis of extremes

Structural time series analysis of 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Research Article Structural time series analysis of extremes Bastiaan Aelbrecht, Stijn Luca This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6271775/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 This study presents a data-driven analysis of non-stationary temperature extremes using a structural time series approach combined with Extreme Value Theory (EVT). We extend the Dynamic Generalized Extreme Value (DGEV) framework by integrating an Expectation-Maximization (EM) algorithm for robust parameter estimation. Using over a century of daily temperature records from Uccle, Belgium, we model seasonal extremes and averages within a state-space framework, where the location parameter evolves stochastically. The structural approach allows us to explicitly separate trend and seasonality, providing deeper insights into long-term changes in extreme temperatures. Our results reveal a significant warming trend, with extreme minimum temperatures increasing more rapidly than maximum temperatures. Additionally, we show a quick emergence of previously impossible events following a power-law growth, emphasizing the increasing likelihood of record-breaking extremes. We compare our structural dynamic models to traditional non-dynamic models, demonstrating that allowing time-varying parameters improves the representation of climate trends. The model is validated through sequential Monte Carlo estimation and goodness-of-fit diagnostics, highlighting the effectiveness of our approach in capturing the evolving distribution of extremes. These findings underscore the importance of structural modeling in extreme value analysis and provide a flexible, data-driven framework for studying climate-driven changes in extremes. Extreme Value Theory Sequential Monte Carlo State Space Models EM algorithm Climate Change 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. 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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-6271775","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438171696,"identity":"29347807-a9a0-46d7-b6ae-b49643ffe12a","order_by":0,"name":"Bastiaan Aelbrecht","email":"data:image/png;base64,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","orcid":"","institution":"Ghent University","correspondingAuthor":true,"prefix":"","firstName":"Bastiaan","middleName":"","lastName":"Aelbrecht","suffix":""},{"id":438171697,"identity":"d7940fbc-c997-4b86-b058-eea83f0720da","order_by":1,"name":"Stijn Luca","email":"","orcid":"","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Stijn","middleName":"","lastName":"Luca","suffix":""}],"badges":[],"createdAt":"2025-03-20 17:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6271775/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6271775/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83940351,"identity":"672ad2df-6f11-4dcc-ad3a-bf6676d112e9","added_by":"auto","created_at":"2025-06-04 18:01:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2887964,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6271775/v1_covered_29a40c25-969a-47de-8e83-e3971e831592.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural time series analysis of extremes","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":"Extreme Value Theory, Sequential Monte Carlo, State Space Models, EM algorithm, Climate Change","lastPublishedDoi":"10.21203/rs.3.rs-6271775/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6271775/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a data-driven analysis of non-stationary temperature extremes using a structural time series approach combined with Extreme Value Theory (EVT). 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