Accessible Statistical Model for Long-Range ENSO Forecasting

Accessible Statistical Model for Long-Range ENSO Forecasting | 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 Accessible Statistical Model for Long-Range ENSO Forecasting Sri Asriyanti, Halmar Halide This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8152771/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 Forecasting the El Niño–Southern Oscillation (ENSO) remains a central challenge in seasonal climate prediction, with far‑reaching consequences for agriculture, water resources, and disaster preparedness. While dynamical models provide skillful forecasts, their reliance on supercomputing limits accessibility in many regions. This study introduces IndOzy‑LR, a parsimonious linear regression model that employs five lagged predictors of Niño 3.4 sea surface temperature anomalies to generate forecasts up to 11 seasonal leads. Results show that IndOzy‑LR achieves high accuracy at short horizons and retains useful skill through lead 8, performing competitively with a state‑of‑the‑art deep learning benchmark. The model further anticipates a neutral ENSO phase for 2025–2026, consistent with ensemble dynamical and statistical outlooks from the International Research Institute (IRI). These findings highlight the potential of low‑cost statistical approaches to extend forecast horizons, democratize access to seasonal climate prediction, and strengthen resilience in regions with limited forecasting infrastructure. Long-range Seasonal ENSO Forecasting Sea Surface Temperature Anomaly Niño 3.4 Statistical models ENSO models skills Climate Prediction Full Text 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. 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-8152771","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556501182,"identity":"d7dadb00-717c-4894-aeda-3f7cb7bd93b5","order_by":0,"name":"Sri Asriyanti","email":"","orcid":"","institution":"Hasanuddin University: Universitas Hasanuddin","correspondingAuthor":false,"prefix":"","firstName":"Sri","middleName":"","lastName":"Asriyanti","suffix":""},{"id":556501183,"identity":"8faae11e-f58c-408e-bb83-31dc89bea66e","order_by":1,"name":"Halmar 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