Toward Spatially Sharper Precipitation Prediction via Global-Local Frequency Guidance

Toward Spatially Sharper Precipitation Prediction via Global-Local Frequency Guidance | 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 Toward Spatially Sharper Precipitation Prediction via Global-Local Frequency Guidance Subin An, Youngwook Kim, Dongjin Cho, Yoo-Geun Ham This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9166093/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Precipitation prediction is critical for water-related hazard preparedness, and deep learning has shown strong potential. However, conventional mean squared error (MSE) training oversmooths high-frequency variability, producing blurred precipitation fields. Although frequency-domain losses such as Fourier Amplitude and Correlation Loss (FACL) enhance global spectral consistency, they lack constraints on local phase coherence. To address this, we develop the Wavelet-Fourier Composite Loss (WFCL), integrating Wavelet Amplitude and Correlation Loss (WACL) based on the Dual-Tree Complex Wavelet Transform with FACL to jointly enforce global spectral alignment and localized phase coherence. Experiments with a U-Net show WFCL consistently outperforms MSE and FACL, maintaining stable performance across 1–3 day lead times (2018–2023). It reduces lead-time-averaged Learned Perceptual Image Patch Similarity from 0.1980 to 0.1067 relative to MSE and improves Local Phase Coherence by about 21%. In addition, for 1-day prediction, WFCL reduces Regional Histogram Distance by approximately 24% locally and 52% globally compared with FACL. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Precipitation Prediction Deep Learning High-Frequency Preservation Composite Loss Function Wavelet-Fourier Composite Loss (WFCL) Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 18 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor assigned by journal 21 Mar, 2026 Submission checks completed at journal 21 Mar, 2026 First submitted to journal 18 Mar, 2026 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. 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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-9166093","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611501287,"identity":"c8a27e35-ba8e-4a09-93f2-65a293b945e2","order_by":0,"name":"Subin An","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Subin","middleName":"","lastName":"An","suffix":""},{"id":611501288,"identity":"a5d0ae60-c185-49dc-a68f-6e48a1038183","order_by":1,"name":"Youngwook Kim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Youngwook","middleName":"","lastName":"Kim","suffix":""},{"id":611501289,"identity":"9575252e-29d1-4fb5-b682-b6db688fe9cb","order_by":2,"name":"Dongjin Cho","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Dongjin","middleName":"","lastName":"Cho","suffix":""},{"id":611501290,"identity":"9b5ba04a-07c1-4c08-8bce-2eb7d0cd0d32","order_by":3,"name":"Yoo-Geun Ham","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBADORjDgGgtxqRrSWwgWovB8ebDLz623UmfP7vHgOFHDYOxeQMhLWeOpVnObHuWu+HOGQPGnmMMZjIHCGm5kWNmzNt2OHeDRI4BA28Dg40EQYeBtPxtO5wuPyPHgPEvkVqMHzO2HU5guJFjwAy0xYygFkmgXxh7zh023HAjreCwzDEJY4Ja+IAh9uFH2WF5+RnJGx++qbExnEFIi8IBBja4uQcYGAjawcAg38DA/IGwslEwCkbBKBjRAABEuEByztWnMwAAAABJRU5ErkJggg==","orcid":"","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Yoo-Geun","middleName":"","lastName":"Ham","suffix":""}],"badges":[],"createdAt":"2026-03-19 07:10:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9166093/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9166093/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566011,"identity":"3cc53b37-3d47-4fa3-998e-8cbcc873c9f4","added_by":"auto","created_at":"2026-03-27 12:55:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1383959,"visible":true,"origin":"","legend":"","description":"","filename":"MSTowardSpatiallySharperPrecipitationPredictionviaGlobalLocalFrequencyGuidanceff.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9166093/v1_covered_dfca7c6f-7ed0-41df-b7cf-eb1e3a6e954b.pdf"},{"id":105436516,"identity":"d0d05eb8-c81c-44a9-9606-100a8e1a78ee","added_by":"auto","created_at":"2026-03-26 04:15:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2595968,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9166093/v1/c4899fd7b1e0645bd2820273.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Toward Spatially Sharper Precipitation Prediction via Global-Local Frequency Guidance","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Precipitation Prediction, Deep Learning, High-Frequency Preservation, Composite Loss Function, Wavelet-Fourier Composite Loss (WFCL)","lastPublishedDoi":"10.21203/rs.3.rs-9166093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9166093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrecipitation prediction is critical for water-related hazard preparedness, and deep learning has shown strong potential. However, conventional mean squared error (MSE) training oversmooths high-frequency variability, producing blurred precipitation fields. Although frequency-domain losses such as Fourier Amplitude and Correlation Loss (FACL) enhance global spectral consistency, they lack constraints on local phase coherence. To address this, we develop the Wavelet-Fourier Composite Loss (WFCL), integrating Wavelet Amplitude and Correlation Loss (WACL) based on the Dual-Tree Complex Wavelet Transform with FACL to jointly enforce global spectral alignment and localized phase coherence. Experiments with a U-Net show WFCL consistently outperforms MSE and FACL, maintaining stable performance across 1\u0026ndash;3 day lead times (2018\u0026ndash;2023). It reduces lead-time-averaged Learned Perceptual Image Patch Similarity from 0.1980 to 0.1067 relative to MSE and improves Local Phase Coherence by about 21%. 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