Climate-Conditioned Catastrophe Modeling for Dynamic Risk Assessment

Climate-Conditioned Catastrophe Modeling for Dynamic Risk Assessment | 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 Climate-Conditioned Catastrophe Modeling for Dynamic Risk Assessment Francesco Comola, Ian Bolliger, Yann Boulben Meyer, Bernhard Märtl, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9124834/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Seasonal climate variability strongly influences North Atlantic hurricane activity, yet catastrophe models used across risk transfer markets typically assume a stationary long-term climatology. This limits the ability of risk-transfer systems — spanning insurance, reinsurance, capital markets, and public risk financing — to anticipate predictable fluctuations in hazard and loss potential. We introduce a climate-conditioned catastrophe modeling framework that integrates probabilistic seasonal hurricane forecasts with stochastic event sets to generate dynamically updated loss distributions. The framework combines a climate-forced tropical cyclone generator with a statistical resampling engine that adjusts event frequency, intensity, and landfall patterns in catastrophe model output to reflect the expected climate state of each season. Applying this approach to a 40-year out-of-sample evaluation, we show that climate-conditioned loss distributions substantially improve the performance and resilience of insurance-linked securities portfolios. Dynamic portfolios informed by seasonal climate states achieve up to 30% higher mean returns and 50% smaller drawdowns during high-loss years compared with portfolios based on static climatology. These results demonstrate that seasonal climate information can be operationalized to enhance financial stability in sectors exposed to weather extremes. The methodology is hazard-agnostic and extensible to other climate-sensitive perils. Climate Analysis and Modeling Full Text Additional Declarations The authors declare potential competing interests as follows: Ian Bolliger and Jamie Rodney are employees of, and own equity in, Reask. Reask is the provider of seasonal hurricane event sets and the CBRA package used in this study. Supplementary Files supp.pdf Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-9124834","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607545608,"identity":"09205b8d-4712-416c-b495-348fb317f2f8","order_by":0,"name":"Francesco Comola","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBAC9gZUvgUDPzsBLTwHUPkSDJLNDIwN2JTi1mJwmJAW6cPPHvyoqJPnZ2B++OhGhYS88WH26w9+7mCQ5xc7gF0LX5q5Yc+Zw4YzG9iMjXPOSBhuO8xT2Nh7hsFw5uwErFrseRjMpBnbDiQYHOBhk85tk2AEaklsZmxjSDC4jV0LDw/7N2nGf3VwLfabmwlq4QHa0sAM15K4gZn9ICEtZZI9x4B+aYb4JXnGYR7Gmb1AT+HyC9Bh2yR+1ABDjL354eOcChvb/vb2Bx9+7rCR55fGrgUBmBHGGABjRoKAclTA/oBAZI6CUTAKRsEIAwDFhlQriUvODgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3867-732X","institution":"LGT ILS Partners","correspondingAuthor":true,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Comola","suffix":""},{"id":607545609,"identity":"d7ada5d0-dab0-4cc2-91d6-b94145ecb08c","order_by":1,"name":"Ian Bolliger","email":"","orcid":"https://orcid.org/0000-0001-8055-297X","institution":"Reask","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Bolliger","suffix":""},{"id":607545613,"identity":"0dede3c0-c45a-48de-95f0-53051f11b8e5","order_by":2,"name":"Yann Boulben Meyer","email":"","orcid":"","institution":"LGT ILS Partners","correspondingAuthor":false,"prefix":"","firstName":"Yann","middleName":"Boulben","lastName":"Meyer","suffix":""},{"id":607545615,"identity":"dec48c27-1a19-4f3f-8083-ea218c7eddf5","order_by":3,"name":"Bernhard Märtl","email":"","orcid":"","institution":"LGT ILS Partners","correspondingAuthor":false,"prefix":"","firstName":"Bernhard","middleName":"","lastName":"Märtl","suffix":""},{"id":607545619,"identity":"15cd83af-7e91-4830-ab02-c194ba842754","order_by":4,"name":"Jamie Rodney","email":"","orcid":"","institution":"Reask","correspondingAuthor":false,"prefix":"","firstName":"Jamie","middleName":"","lastName":"Rodney","suffix":""},{"id":607545621,"identity":"548f827f-58fd-4a30-bfe1-d65df74bafe7","order_by":5,"name":"Hilary Paul","email":"","orcid":"","institution":"LGT ILS Partners","correspondingAuthor":false,"prefix":"","firstName":"Hilary","middleName":"","lastName":"Paul","suffix":""}],"badges":[],"createdAt":"2026-03-14 20:10:28","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9124834/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-9124834/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105904893,"identity":"4ba7911d-cece-4065-9633-49d00bc4942b","added_by":"auto","created_at":"2026-04-01 10:10:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1009902,"visible":true,"origin":"","legend":"","description":"","filename":"repoR2v2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9124834/v2_covered_4d0cebcc-f441-4220-bb32-78a1c079ea89.pdf"},{"id":105846732,"identity":"3f4af05b-3871-4e22-9b53-6efc3ce42bce","added_by":"auto","created_at":"2026-03-31 18:07:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":401685,"visible":true,"origin":"","legend":"","description":"","filename":"supp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9124834/v2/22ae99913dc6207bad0b5bd1.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: Ian Bolliger and Jamie Rodney are employees of, and own equity in, Reask. 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