Explainable Machine Learning for Climate Change Hotspot Identification: Spatial Generalization Testing in South Asian Region

Explainable Machine Learning for Climate Change Hotspot Identification: Spatial Generalization Testing in South Asian Region | 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 Explainable Machine Learning for Climate Change Hotspot Identification: Spatial Generalization Testing in South Asian Region Ram Chand, Saeeddudin Sheikh, Barkha Kanjwani, Saqia Bukhari, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8726793/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 Machine learning (ML) continues to be utilized in climate-change research, and much of the analysis overestimates model performance, as it does not include spatial dependence. This methodological inadequacy creates the misleading impression that models generalize well to new locations, when in fact they often fail outside the training domain. We evaluate eight ML predictors of temperature anomalies in 22 districts of Sindh, Pakistan on 44 years of observations. We performed spatial generalization on a Leave-One-District-Out Cross-Validation (LODO-CV) and tested this generalization to completely unfamiliar locations. Gradient Boosting was the most successful algorithm with ($R^2 = 0.914\pm 0.098$) when predicting the temperature anomaly in areas that were not included in the training, which indicates a strong transferability to the wide range of climatic areas across the region. SHAP feature attribution showed that climate variables (37.6%), temporal trends (32.0%), and anthropogenic proxies (23.7%), are the most important predictors, although it is also important to note the caveat that the importance of proxies is only indicative of correlation, not causation, and must be carefully considered when applying to policy matters. Part of dependence analysis estimated a negative dependence of vegetation-temperature of $-0.15^{\circ}$C per 0.1 NDVI of vegetation increase indicating that vegetation preservation and restoration measures may provide cooling advantages. By using a dual-index model, which integrates the frequency of extreme events with average climate changes, we were able to pinpoint seven hotspots of climate change, concentrated in Karachi and Hyderabad urban areas, which are exposed to compound risk of urbanization, coastal exposure, and rising temperature extremes. The results indicate the urgent need of spatially explicit validation procedures when using climate ML and offer practical suggestions to specific adaptation planning to the most climate-prone districts in Pakistan. machine learning climate change hotspot identification spatial cross-validation explainable AI deep learning temperature anomaly Full Text Additional Declarations No competing interests reported. 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. 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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-8726793","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582238493,"identity":"d4a42751-6895-4de5-8fac-fc157ee947e4","order_by":0,"name":"Ram Chand","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYLACxgYgcbyN4UBCBQODAfFazhxjPPDgDElabqQxH3zYRoQW8/bTCQw/d9jl8R1ISziQOO+wvDl78wGGHxXbcGqROZO7gbH3THKx5IFjBw4kbjtsuLPnWAJjz5nbOLVIMORuYGZsY07ccLC9AaSFccONHAOgCB4t/G9BWuoTNxxmB2qZc9iesBYJsC2HEzccYwM6rAHIIKzl7YaDvW3HE2eeYUs4kHAsPXnDmWMJB/H6hT9344OfbdWJffefGX/8UWNtu+F488EHPypwawGBA0jsZgwRgqCOFMWjYBSMglEwQgAA+JFpWYFPCzkAAAAASUVORK5CYII=","orcid":"","institution":"The Begum Nusrat Bhutto Women University","correspondingAuthor":true,"prefix":"","firstName":"Ram","middleName":"","lastName":"Chand","suffix":""},{"id":582238494,"identity":"c53b1cfe-31e9-4fe1-9c55-35a15f399847","order_by":1,"name":"Saeeddudin Sheikh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Saeeddudin","middleName":"","lastName":"Sheikh","suffix":""},{"id":582238495,"identity":"e62ee869-ba1c-4592-9860-f8aec9f121dd","order_by":2,"name":"Barkha Kanjwani","email":"","orcid":"","institution":"The Begum Nusrat Bhutto Women University","correspondingAuthor":false,"prefix":"","firstName":"Barkha","middleName":"","lastName":"Kanjwani","suffix":""},{"id":582238496,"identity":"c824fae6-9f20-4cd1-8075-a935d8409581","order_by":3,"name":"Saqia Bukhari","email":"","orcid":"","institution":"The Begum Nusrat Bhutto Women University","correspondingAuthor":false,"prefix":"","firstName":"Saqia","middleName":"","lastName":"Bukhari","suffix":""},{"id":582238497,"identity":"612d105d-97bc-47e5-b217-e0362a1fa9a9","order_by":4,"name":"Harsh Kumar","email":"","orcid":"","institution":"Mehran University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Harsh","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2026-01-29 04:09:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8726793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8726793/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102296680,"identity":"ad4e1cd0-b2df-4933-adb6-077fb80418f1","added_by":"auto","created_at":"2026-02-10 10:20:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9015090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptlatex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8726793/v1_covered_f9867b16-d3d7-4797-8ef0-15d2c2e4f98f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainable Machine Learning for Climate Change Hotspot Identification: Spatial Generalization Testing in South Asian Region","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"machine learning, climate change, hotspot identification, spatial cross-validation, explainable AI, deep learning, temperature anomaly","lastPublishedDoi":"10.21203/rs.3.rs-8726793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8726793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Machine learning (ML) continues to be utilized in climate-change research, and much of the analysis overestimates model performance, as it does not include spatial dependence. This methodological inadequacy creates the misleading impression that models generalize well to new locations, when in fact they often fail outside the training domain. We evaluate eight ML predictors of temperature anomalies in 22 districts of Sindh, Pakistan on 44 years of observations. We performed spatial generalization on a Leave-One-District-Out Cross-Validation (LODO-CV) and tested this generalization to completely unfamiliar locations. Gradient Boosting was the most successful algorithm with ($R^2 = 0.914\\pm 0.098$) when predicting the temperature anomaly in areas that were not included in the training, which indicates a strong transferability to the wide range of climatic areas across the region. 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