Evaluation of Climate Prediction Models in Yunnan, China: Traditional Methods and AI Approaches

Evaluation of Climate Prediction Models in Yunnan, China: Traditional Methods and AI Approaches | 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 Evaluation of Climate Prediction Models in Yunnan, China: Traditional Methods and AI Approaches Junfan Zhao, Fan Zhao, Hang Deng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7356043/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Understanding regional climate variability is essential for effective climate risk management, particularly in areas with complex terrain like Yunnan Province, China. Traditional regional climate models (RCMs), such as RegCM, face limitations in predictive accuracy and computational efficiency due to their reliance on nonlinear physical simulations. To address these challenges, this study introduces a comprehensive framework to evaluate regional climate predictions using artificial intelligence (AI) models. Specifically, we assess the performance of five mainstream AI models—CNN, LSTM, Transformer, CNN-LSTM, and LSTM-Transformer—in predicting key climate variables: temperature, precipitation, and relative humidity. Daily meteorological observations from 25 national stations (2004–2018) were employed, with dimensionality reduction and temporal feature encoding enhancing the sequence-based learning models. Model performance was evaluated using RMSE, MAE, and Pearson correlation coefficient (R). The results demonstrate that AI models substantially outperform RegCM, particularly for temperature and humidity predictions. Among them, the LSTM-Transformer achieved the highest accuracy in temperature (RMSE = 0.7410, R = 0.9938) and humidity (RMSE = 3.7054, R = 0.9710), while CNN-LSTM was most effective for precipitation (RMSE = 4.7260, R = 0.8559). These findings highlight the potential of artificial intelligence for advancing multivariate climate prediction in regions with significant spatial heterogeneity, providing a data-driven basis for more accurate climate risk assessment and early warning applications. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Climate prediction Yunnan region Regional climate model Artificial intelligence LSTM-Transformer Full Text Additional Declarations No competing interests reported. Supplementary Files Attachment1.pdf Attachment2.pdf Cite Share Download PDF Status: Published Journal Publication published 08 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviews received at journal 14 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers invited by journal 27 Aug, 2025 Editor assigned by journal 27 Aug, 2025 Editor invited by journal 20 Aug, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 18 Aug, 2025 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-7356043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":507705391,"identity":"919900a6-d145-4a58-ac32-6b9fe9951ef6","order_by":0,"name":"Junfan Zhao","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Junfan","middleName":"","lastName":"Zhao","suffix":""},{"id":507705392,"identity":"5be76cef-82fe-4d4a-b536-945edfc7485a","order_by":1,"name":"Fan Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBAC9gYeIFkBYgBpHmK08BwAKTsDYpCkhbGNJC38Zw9++DivLrFHIoHxwds2BnlzgloYziVLztx2GKSF2XBuG4PhzgYCWuwZewykebcdyN0vkcAmzdvGkGBwgJAtzDzGv3nn1OUCbWH/TZwWNh4zad4GZpAWNmbitPDwmFnOOHa4vofnYbPknHMShhsIauE/Y3zjQ02dMQ978sEPb8ps5AnaggQYG4CEBPHqR8EoGAWjYBTgBgCJRDng+LmfhAAAAABJRU5ErkJggg==","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Fan","middleName":"","lastName":"Zhao","suffix":""},{"id":507705393,"identity":"b5d7dcf6-5d40-4e31-9f74-3b7e55577b1b","order_by":2,"name":"Hang Deng","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2025-08-12 13:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7356043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7356043/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-27326-w","type":"published","date":"2025-12-08T15:58:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98245168,"identity":"ef951f07-be47-41a7-830c-55ca08584946","added_by":"auto","created_at":"2025-12-15 16:17:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1425178,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7356043/v1_covered_a008d08a-cf34-4853-9fd8-c3c27a837621.pdf"},{"id":90557513,"identity":"1546501a-e7ea-438d-8250-baaf3bd511d4","added_by":"auto","created_at":"2025-09-04 04:52:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2416777,"visible":true,"origin":"","legend":"","description":"","filename":"Attachment1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7356043/v1/27d53cc22e1c0b50f08cdbb3.pdf"},{"id":90557509,"identity":"e0eda8ae-cce4-4124-9d8b-f0aa21f72957","added_by":"auto","created_at":"2025-09-04 04:52:24","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":171706,"visible":true,"origin":"","legend":"","description":"","filename":"Attachment2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7356043/v1/6fb271e1ab72048a068cfa60.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Climate Prediction Models in Yunnan, China: Traditional Methods and AI Approaches","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate prediction, Yunnan region, Regional climate model, Artificial intelligence, LSTM-Transformer","lastPublishedDoi":"10.21203/rs.3.rs-7356043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7356043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding regional climate variability is essential for effective climate risk management, particularly in areas with complex terrain like Yunnan Province, China. Traditional regional climate models (RCMs), such as RegCM, face limitations in predictive accuracy and computational efficiency due to their reliance on nonlinear physical simulations. To address these challenges, this study introduces a comprehensive framework to evaluate regional climate predictions using artificial intelligence (AI) models. Specifically, we assess the performance of five mainstream AI models\u0026mdash;CNN, LSTM, Transformer, CNN-LSTM, and LSTM-Transformer\u0026mdash;in predicting key climate variables: temperature, precipitation, and relative humidity. Daily meteorological observations from 25 national stations (2004\u0026ndash;2018) were employed, with dimensionality reduction and temporal feature encoding enhancing the sequence-based learning models. Model performance was evaluated using RMSE, MAE, and Pearson correlation coefficient (R). The results demonstrate that AI models substantially outperform RegCM, particularly for temperature and humidity predictions. Among them, the LSTM-Transformer achieved the highest accuracy in temperature (RMSE\u0026thinsp;=\u0026thinsp;0.7410, R\u0026thinsp;=\u0026thinsp;0.9938) and humidity (RMSE\u0026thinsp;=\u0026thinsp;3.7054, R\u0026thinsp;=\u0026thinsp;0.9710), while CNN-LSTM was most effective for precipitation (RMSE\u0026thinsp;=\u0026thinsp;4.7260, R\u0026thinsp;=\u0026thinsp;0.8559). These findings highlight the potential of artificial intelligence for advancing multivariate climate prediction in regions with significant spatial heterogeneity, providing a data-driven basis for more accurate climate risk assessment and early warning applications.\u003c/p\u003e","manuscriptTitle":"Evaluation of Climate Prediction Models in Yunnan, China: Traditional Methods and AI Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 04:44:19","doi":"10.21203/rs.3.rs-7356043/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T16:10:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T10:02:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T05:29:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103282501224379610961630923229763018846","date":"2025-09-07T08:52:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166832528840161759227803610471067382430","date":"2025-08-29T04:32:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-28T03:11:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-28T03:10:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-20T08:18:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-18T06:43:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-18T06:39:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"142a958e-0ad1-4a43-bfbf-be4955e6e9e7","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53930204,"name":"Earth and environmental sciences/Climate sciences"},{"id":53930205,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-12-15T16:13:44+00:00","versionOfRecord":{"articleIdentity":"rs-7356043","link":"https://doi.org/10.1038/s41598-025-27326-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-08 15:58:14","publishedOnDateReadable":"December 8th, 2025"},"versionCreatedAt":"2025-09-04 04:44:19","video":"","vorDoi":"10.1038/s41598-025-27326-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27326-w","workflowStages":[]},"version":"v1","identity":"rs-7356043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7356043","identity":"rs-7356043","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}