A Global Probabilistic Framework for Meteorological Drought Risk Assessment Using Self-Calibrating PDSI and Stochastic Simulation

A Global Probabilistic Framework for Meteorological Drought Risk Assessment Using Self-Calibrating PDSI and Stochastic Simulation | 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 Global Probabilistic Framework for Meteorological Drought Risk Assessment Using Self-Calibrating PDSI and Stochastic Simulation Chen Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7566790/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 Drought is one of the most consequential natural hazards, with wide-ranging impacts on ecosystems, economies, and societies. Assessing drought risk under current and future climate conditions is essential for adaptation planning and financial risk management, but remains difficult due to the complexity of climate variability and uncertainty in projections. This study develops a global probabilistic framework for estimating meteorological drought return periods that integrates climate data with statistical modeling. The framework combines the self-calibrating Palmer Drought Severity Index, a stochastic weather generator, and generalized extreme value analysis to evaluate drought duration extremes at a 2.5° × 2.5° global resolution. The weather generator reproduces observed variability, persistence, and long-term trends, creating 1,000 synthetic time series per grid cell to enable robust probabilistic assessment. Future projections are derived by scaling the synthetic series across three socioeconomic pathways representing low, medium, and high greenhouse gas emissions (SSP1-2.6, SSP2-4.5, and SSP5-8.5), using temperature adjustment factors from a multi-model climate ensemble. Post-processing corrections are applied to address statistical limitations such as persistence effects and nonlinear behavior in extreme droughts. The results demonstrate broad agreement with climate model ensembles while identifying regions where nonlinear dynamics drive divergences. This framework provides spatially explicit estimates of drought risk, offering a practical tool for decision-makers in climate adaptation, insurance, and financial sectors. More broadly, the study highlights the value of probabilistic approaches that integrate observational statistics with climate projections to strengthen resilience in a warming world. Global Probabilistic Drought Risk Assessment Self-calibrating Palmer Drought Severity Index (scPDSI) Stochastic Weather Generator (SWG) Climate Risk Financial Modeling (CRFM) Full Text 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-7566790","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530057175,"identity":"16449706-ebf1-4cd9-9da7-eba5b4f8147e","order_by":0,"name":"Chen 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Stochastic Weather Generator (SWG), Climate Risk Financial Modeling (CRFM)","lastPublishedDoi":"10.21203/rs.3.rs-7566790/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7566790/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought is one of the most consequential natural hazards, with wide-ranging impacts on ecosystems, economies, and societies. Assessing drought risk under current and future climate conditions is essential for adaptation planning and financial risk management, but remains difficult due to the complexity of climate variability and uncertainty in projections. This study develops a global probabilistic framework for estimating meteorological drought return periods that integrates climate data with statistical modeling.\u003c/p\u003e\u003cp\u003eThe framework combines the self-calibrating Palmer Drought Severity Index, a stochastic weather generator, and generalized extreme value analysis to evaluate drought duration extremes at a 2.5\u0026deg; \u0026times; 2.5\u0026deg; global resolution. The weather generator reproduces observed variability, persistence, and long-term trends, creating 1,000 synthetic time series per grid cell to enable robust probabilistic assessment. Future projections are derived by scaling the synthetic series across three socioeconomic pathways representing low, medium, and high greenhouse gas emissions (SSP1-2.6, SSP2-4.5, and SSP5-8.5), using temperature adjustment factors from a multi-model climate ensemble. Post-processing corrections are applied to address statistical limitations such as persistence effects and nonlinear behavior in extreme droughts.\u003c/p\u003e\u003cp\u003eThe results demonstrate broad agreement with climate model ensembles while identifying regions where nonlinear dynamics drive divergences. This framework provides spatially explicit estimates of drought risk, offering a practical tool for decision-makers in climate adaptation, insurance, and financial sectors. 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