A Systematic Approach to Modeling Monthly Maximum Temperature and Total Rainfall in Kenya

A Systematic Approach to Modeling Monthly Maximum Temperature and Total Rainfall in Kenya | 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 A Systematic Approach to Modeling Monthly Maximum Temperature and Total Rainfall in Kenya Kevin Otieno, Benard Omolo, Linda Chaba, Collins Odhiambo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5831109/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Goodness of fit (GOF) test approaches for selecting probability distributions of climatic variables are pervasive in the statisticalliterature. However, a combined approach of multiple tests remains underutilized, despite evidence supporting their improvedprecision. Increased erratic climatic conditions pose severe threats to economic stability, necessitating robust statisticalmethods for climate modeling. To address this need, this study evaluates probability distributions for climatic variables using acomprehensive approach that combines multiple tests. A scoring system ranked each distribution’s performance across tests,with a composite score indicating the best fit. To assess robustness, sensitivity analysis on the best-performing distributionexamined the influence of partitioning data into different segments (block sizes). The results show a generalized extremevalue (GEV) distribution consistently outperforming other distributions for temperature and rainfall data, across multiple metrics. Longer block sizes capture long-term climatic patterns but introduce greater uncertainty due to fewer data points, while shorterblock sizes tend to overfit. Intermediate block sizes provide a balance, producing reliable parameter estimates and stable returnlevels. These findings underscore the importance of selecting suitable block sizes and confirm the robustness of the GEVdistribution for climate modeling. The study contributes to improved methodologies for risk assessment and climate adaptationstrategies, particularly in regions such as Kenya. Earth and environmental sciences/Climate sciences/Hydrology Physical sciences/Mathematics and computing/Statistics Earth and environmental sciences/Climate sciences/Climate change/Climate and earth system modelling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 May, 2025 Reviews received at journal 15 May, 2025 Reviews received at journal 08 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers invited by journal 04 Apr, 2025 Submission checks completed at journal 26 Mar, 2025 First submitted to journal 19 Mar, 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. 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-5831109","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440014337,"identity":"def5b681-038b-45e3-b735-0b0361ede656","order_by":0,"name":"Kevin Otieno","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACCSA+AMRyYB5jA1SYB68WZrAWYwY2IHmQWC0gkNhAtBbJ9v6DBz7usEmfP7/34eePO7bJmbc3MD5424ZbizTPYYaDM8+k5W44xm4scfDMbWOZMweYDefi0SInkcxwmLftcO4GNjYGiYNttxNnSCSwSfMS0vK37XC6fBsb8w+wFvkH7L/xaZEGaWFsO5zAcIyNDWoLAxszPi2SPYcNDva2pRluOJbGZnG27baxBE9is+Scc7i1SBxvfPzhZ5uNvHzzMeYblW235STYDx/88KYMtxZsAJ4GRsEoGAWjYBSQCwBvO1Q+tBegXgAAAABJRU5ErkJggg==","orcid":"","institution":"Strathmore University","correspondingAuthor":true,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Otieno","suffix":""},{"id":440014338,"identity":"659d1d9c-c6c2-46be-99fc-6f5b6a095ffe","order_by":1,"name":"Benard Omolo","email":"","orcid":"","institution":"University of South Carolina Upstate","correspondingAuthor":false,"prefix":"","firstName":"Benard","middleName":"","lastName":"Omolo","suffix":""},{"id":440014339,"identity":"82add768-2c02-4788-aae3-6da03f2b2f1a","order_by":2,"name":"Linda Chaba","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Linda","middleName":"","lastName":"Chaba","suffix":""},{"id":440014340,"identity":"89054605-7ad0-441d-89e3-feffd599b32a","order_by":3,"name":"Collins Odhiambo","email":"","orcid":"","institution":"University of Illinois at Chicago","correspondingAuthor":false,"prefix":"","firstName":"Collins","middleName":"","lastName":"Odhiambo","suffix":""}],"badges":[],"createdAt":"2025-01-15 04:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5831109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5831109/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-12810-0","type":"published","date":"2025-08-28T15:58:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90345005,"identity":"1dcb4cb9-7cf0-4c04-b661-7c75a2d76982","added_by":"auto","created_at":"2025-09-01 16:09:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1318979,"visible":true,"origin":"","legend":"","description":"","filename":"KevinOtienorevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5831109/v1_covered_7e639d75-89b8-4a53-9e95-405571b884fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Systematic Approach to Modeling Monthly Maximum Temperature and Total Rainfall in Kenya","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":"","lastPublishedDoi":"10.21203/rs.3.rs-5831109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5831109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Goodness of fit (GOF) test approaches for selecting probability distributions of climatic variables are pervasive in the statisticalliterature. 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