Assessing the Predictive Skill of Global Climate Models for Long and Short Rains in the Greater Horn of Africa

Assessing the Predictive Skill of Global Climate Models for Long and Short Rains in the Greater Horn of Africa | 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 Assessing the Predictive Skill of Global Climate Models for Long and Short Rains in the Greater Horn of Africa Athanase Hafashimana, Mouhamadou Bamba Sylla, Philibert Nsengiyumva, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7542175/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 Seasonal forecasts play a crucial role in delivering early warnings to various sectors, particularly the agricultural sector. The Greater Horn of Africa region depends on rainfed agriculture, hence the need for accurate forecasts. This study uses Global Climate Models (GCMs) and satellite precipitation observations to assess the predictability of observed precipitation by deploying traditional machine learning algorithms and deep learning models. We compare the predictability of long and short rainy seasons in the region. The results highlight the challenges of forecasting the long rains season, with traditional machine learning algorithms showing low feature importance. In contrast, short rains can be predicted and achieved with high accuracy using both traditional machine learning models and deep learning architectures, particularly Long Short-Term Memory (LSTM) networks. In this study, we used ten Global Climate Models as input features for seasonal climatological forecasts, with a single output feature derived from observations of the Global Precipitation Climatology Center (GPCC) over a 30-year period (1990-2019). We measured the level of explained variance of this set of GCMs. Regardless of the method, high explainable variability was achieved in short rains, and the European Centre for Medium-Range Weather Forecasts (ECMWF) was the best predictor in the region for long rains. On the other hand, the National Aeronautics and Space Administration (NASA) was the most significant contributor to the predictions for short rains. Global Climate Models Seasonal Forecasts Short Rains Long Rains Machine Learning Full Text Additional Declarations The authors declare no competing interests. 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-7542175","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510653500,"identity":"0494b829-e55e-4476-8d3b-6b3f0f27df04","order_by":0,"name":"Athanase 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