Beyond Accuracy: Evaluating the Data-Driven, Process-Informed DHR-SDP Model for Rainfall Prediction and Climate Signal Representation

Beyond Accuracy: Evaluating the Data-Driven, Process-Informed DHR-SDP Model for Rainfall Prediction and Climate Signal Representation | 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 Beyond Accuracy: Evaluating the Data-Driven, Process-Informed DHR-SDP Model for Rainfall Prediction and Climate Signal Representation Daniel E. Mendoza, Ana Ochoa-Sánchez, Santiago Núñez-Mejía, Esteban P. Samaniego This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7651876/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Rainfall prediction is essential for understanding and mitigating the impacts of global environmental change. Dynamic and statistical models are used for this purpose: dynamic models simulate underlying physical processes but are computationally intensive, while statistical models are usually more cost-efficient. Stochastic climate models that are both data-driven and process-informed are interesting, as they often show enhanced predictive capabilities over conventional statistical models, partly due to their ability to capture the inherent climate dynamics. Ensuring the reliability of both predictions and their conceptual interpretations (e.g., descriptions of climate patterns) requires a robust evaluation that extends beyond assessing predictive accuracy. Here, we rigorously assess a data-driven, process-informed rainfall model—DHR-SDP (Dynamic Harmonic Regression with State-Dependent Parameters)—against mean-monthly rainfall estimates, alternative data-driven models with exogenous variables—ARMAX (Autoregressive Moving Average), ANNX (Autoregressive Neural Network), and ARX-SDP (Autoregressive with State-Dependent Parameters)—and nineteen GCM monthly hindcasts at 1° resolution with bias correction. We consider a process-oriented evaluation testing the model’s ability to represent climate patterns (seasonality and trends). This evaluation is conducted in a complex orographic region of the Southern Andes characterized by spatial rainfall variability. Results show that DHR-SDP reproduces seasonal rainfall dynamics, trends, and monthly predictions reliably, due to its capacity to replicate intra- and inter-annual processes. These findings underscore the potential of data-driven, process-informed approaches to predict and conceptualize local climate behavior, even in complex terrains. Further work should examine the model’s broader applicability to climate-change modeling and projections and clarify relationships among global and local climate variables and phenomena. rainfall-prediction data-driven process-informed modelling predictive-accuracy process-oriented evaluation climate signatures Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 Mar, 2026 Reviews received at journal 20 Dec, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 25 Sep, 2025 Submission checks completed at journal 25 Sep, 2025 First submitted to journal 18 Sep, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7651876","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533034905,"identity":"4b1b1d8e-9e2d-4aa7-9223-2b408984c166","order_by":0,"name":"Daniel E. 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Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"rainfall-prediction, data-driven, process-informed modelling, predictive-accuracy, process-oriented evaluation, climate signatures","lastPublishedDoi":"10.21203/rs.3.rs-7651876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7651876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRainfall prediction is essential for understanding and mitigating the impacts of global environmental change. Dynamic and statistical models are used for this purpose: dynamic models simulate underlying physical processes but are computationally intensive, while statistical models are usually more cost-efficient. Stochastic climate models that are both data-driven and process-informed are interesting, as they often show enhanced predictive capabilities over conventional statistical models, partly due to their ability to capture the inherent climate dynamics. Ensuring the reliability of both predictions and their conceptual interpretations (e.g., descriptions of climate patterns) requires a robust evaluation that extends beyond assessing predictive accuracy. Here, we rigorously assess a data-driven, process-informed rainfall model—DHR-SDP (Dynamic Harmonic Regression with State-Dependent Parameters)—against mean-monthly rainfall estimates, alternative data-driven models with exogenous variables—ARMAX (Autoregressive Moving Average), ANNX (Autoregressive Neural Network), and ARX-SDP (Autoregressive with State-Dependent Parameters)—and nineteen GCM monthly hindcasts at 1° resolution with bias correction. We consider a process-oriented evaluation testing the model’s ability to represent climate patterns (seasonality and trends). This evaluation is conducted in a complex orographic region of the Southern Andes characterized by spatial rainfall variability. Results show that DHR-SDP reproduces seasonal rainfall dynamics, trends, and monthly predictions reliably, due to its capacity to replicate intra- and inter-annual processes. These findings underscore the potential of data-driven, process-informed approaches to predict and conceptualize local climate behavior, even in complex terrains. Further work should examine the model’s broader applicability to climate-change modeling and projections and clarify relationships among global and local climate variables and phenomena.\u003c/p\u003e","manuscriptTitle":"Beyond Accuracy: Evaluating the Data-Driven, Process-Informed DHR-SDP Model for Rainfall Prediction and Climate Signal Representation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 15:03:06","doi":"10.21203/rs.3.rs-7651876/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"321653911850708673585820862767933271736","date":"2026-03-11T18:53:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T18:15:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303654475237726268945480845923250998805","date":"2025-11-21T22:47:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T18:09:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T23:13:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T23:13:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2025-09-18T16:58:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d0f9844e-7068-4ce0-b1a4-3f034172a3f4","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-31T15:03:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 15:03:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7651876","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7651876","identity":"rs-7651876","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}