Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction

Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction | 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 Article Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction Jing-Jia Luo, Fenghua Ling, Zeyu Lu, Lei Bai, Swadhin Behera, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3838715/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jun, 2024 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 10 You are reading this latest preprint version Abstract As our planet is entering into the “global boiling” era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding regional dynamical models or statistical downscaling frameworks, are often susceptible to the influence of downscaling uncertainty. Here, we address these limitations by introducing a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1° to 0.1° resolution. Compared with deterministic downscaling schemes, it not only has more accurate local details, but also can generate a large number of ensemble members based on probability distribution sampling to evaluate the uncertainty of downscaling. Additionally, we apply the model to generate a 180-year dataset of monthly surface variables in East Asia, offering a more detailed perspective for understanding local scale climate change over the past centuries. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Climate sciences/Climate change Earth and environmental sciences/Climate sciences/Atmospheric science Full Text Additional Declarations (Not answered) Supplementary Files supplementaryinformation.docx Cite Share Download PDF Status: Published Journal Publication published 13 Jun, 2024 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: revise 11 Feb, 2024 Review # 1 received at journal 07 Feb, 2024 Review # 2 received at journal 27 Jan, 2024 Reviewer # 3 agreed at journal 24 Jan, 2024 Reviewer # 2 agreed at journal 24 Jan, 2024 Reviewer # 1 agreed at journal 23 Jan, 2024 Reviewers invited by journal 19 Jan, 2024 Editor assigned by journal 16 Jan, 2024 Submission checks completed at journal 16 Jan, 2024 First submitted to journal 15 Jan, 2024 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. 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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-3838715","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268018432,"identity":"806afde0-526d-4596-a093-3a68f40c7a72","order_by":0,"name":"Jing-Jia 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