{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:24:55Z","timestamp":1787847895517,"version":"build-2784847793"},"reference-count":125,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Extreme rainfall events (ERES) are among the most challenging hydro-meteorological phenomena to forecast because the complex, nonlinear atmospheric processes involved span multiple spatial and temporal scales and are further intensified by rising climate variability. While Numerical Weather Prediction (NWP) models offer physically consistent representations of atmospheric dynamics, they still struggle to resolve localized convection, rapidly evolving storm systems, and rare, high-intensity precipitation events. This study systematically reviews recent advances in Artificial Intelligence (AI) for extreme rainfall prediction published between 2020 and 2026, using a transparent literature search strategy, predefined screening criteria, quality assessment, and a structured literature review matrix. The review synthesizes evidence from a final evidence base of 139 studies across several dimensions, including deep learning (DL) methodologies, multimodal data fusion, forecasting horizons, operational deployment, uncertainty quantification, and emerging intelligent forecasting paradigms. The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations. Despite notable progress, persistent challenges remain, including data scarcity, class imbalance, model generalization, physical consistency, uncertainty estimation, transferability, and the lack of standardized evaluation frameworks. This review provides a unified synthesis of current methodologies. It identifies key research gaps, highlighting the need for evaluation frameworks that address physical plausibility, uncertainty quantification, reliability, transferability, and operational relevance. The findings indicate that future extreme rainfall prediction systems should advance beyond accuracy-focused evaluation toward integrated, trustworthy, uncertainty-aware, and physically consistent forecasting approaches.<\/jats:p>","DOI":"10.3389\/frai.2026.1908848","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T15:52:51Z","timestamp":1787845971000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning techniques for extreme rainfall prediction: a review of state-of-the-art"],"prefix":"10.3389","volume":"9","author":[{"given":"Braiton U.","family":"Mukhalela","sequence":"first","affiliation":[{"name":"Discipline of Computer Science, School of Agriculture and Science, University of KwaZulu-Natal","place":["Durban, South Africa"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serestina","family":"Viriri","sequence":"additional","affiliation":[{"name":"Discipline of Computer Science, School of Agriculture and Science, University of KwaZulu-Natal","place":["Durban, South Africa"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Ndzi","sequence":"additional","affiliation":[{"name":"School of Electrical and Mechanical Engineering, University of Portsmouth","place":["England, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Gebreslasie","sequence":"additional","affiliation":[{"name":"Discipline of Geography, School of Agriculture and Science, University of KwaZulu-Natal","place":["Durban, South Africa"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Zeeshan Shakir","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland","place":["Paisley, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland","place":["Paisley, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahad","family":"Ayaz","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland","place":["Paisley, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary","family":"Lynch","sequence":"additional","affiliation":[{"name":"Royal College of Surgeons in Ireland, University of Medicine and Health Sciences","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Llinos Haf","family":"Spencer","sequence":"additional","affiliation":[{"name":"Royal College of Surgeons in Ireland, University of Medicine and Health Sciences","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saloshni","family":"Naidoo","sequence":"additional","affiliation":[{"name":"Discipline of Public Health, Institute for Global Health, University of KwaZulu-Natal","place":["Durban, South Africa"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nisha","family":"Nadesan-Reddy","sequence":"additional","affiliation":[{"name":"Discipline of Public Health, Institute for Global Health, University of KwaZulu-Natal","place":["Durban, South Africa"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ozayr","family":"Mahomed","sequence":"additional","affiliation":[{"name":"Discipline of Public Health, Institute for Global Health, University of KwaZulu-Natal","place":["Durban, South Africa"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalie","family":"Dickinson","sequence":"additional","affiliation":[{"name":"School of Health and Life Sciences, University of the West of Scotland","place":["Paisley, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fiona","family":"Henriquez","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering Department, University of Strathclyde","place":["Glasgow, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"5073","DOI":"10.1007\/s41748-025-00813-w","article-title":"An in-depth analysis of approaches for forecasting extreme rainfall events leveraging artificial intelligence, machine learning, and deep learning techniques","volume":"10","author":"Afandi","year":"2025","journal-title":"Earth Syst. 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