Introduction in Artificial Intelligence for Climate Modelling

While numerical weather prediction and climate models remain central to weather and climate services, Artificial Intelligence and Machine Learning are rapidly emerging as complementary or surrogate tools for forecasting, downscaling, bias correction, extreme event detection and impact-based early warning. AI and machine learning approaches can be particularly useful in the African context because they can complement dynamical models, support post-processing and downscaling, and enable the development of practical tools where computing resources, dense observational networks and specialized modelling capacity remain limited. Integration of AI in ECMWF operational weather prediction is as skillful as the traditional dynamical model or even better (Rabier F. et al. 2026). However, many African institutions still face capacity gaps in applying these methods operationally, including limited access to AI expertise, large-scale climate datasets, computing workflows, and practice skills.

Through its continental mandate and strategic plan, ACMAD aims to promote coordinated African capacity in AI-enabled weather and climate modelling across the continent. The 2026 AI theme is therefore timely. It will help ensure that African NHMSs, RCCs, research institutions as well as sectors are not only users of externally developed AI systems, but active contributors to their design, validation, adaptation and operational use in African contexts.

This programme is designed for early- to mid-career professionals working at the intersection of climate science and data. We welcome applicants from National Meteorological and Hydrological Services (NMHSs), universities, and research institutions across Africa who are ready to apply machine learning methods to real climate and weather challenges.