Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives

Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are particularly challenging to predict accurately due to their rarity and chaotic nature, and because of model limitations. However, recent studies have shown that there might be systemic predictability that is not being leveraged, whose exploitation could meet the need for reliable predictions of aggregated extreme weather measures on timescales from weeks to decades ahead. Recently, numerous studies have been devoted to the use of artificial intelligence (AI) to study predictability and make climate predictions. AI techniques have shown great potential to improve the prediction of extreme events and uncover their links to large‐scale and local drivers. Machine and deep learning have been explored to enhance prediction, while causal discovery and explainable AI have been tested to improve our understanding of the processes underlying predictability. Hybrid predictions combining AI, which can reveal unknown spatiotemporal connections from data, with climate models that provide the theoretical foundation and interpretability of the physical world, have shown that improving prediction skills of extremes on climate‐relevant timescales is possible. However, numerous challenges persist in various aspects, including data curation, model uncertainty, generalizability, reproducibility of methods, and workflows. This review aims at overviewing achievements and challenges in the use of AI techniques to improve the prediction of extremes at the subseasonal to decadal timescale. A few best practices are identified to increase trust in these novel techniques, and future perspectives are envisaged for further scientific development. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models The Social Status of Climate Change Knowledge > Climate Science and Decision Making Schematic representation of the AI‐based climate prediction function. Y (target) is a measure of certain aspects of the extreme (e.g., intensity, frequency, etc.). Xpreds (predictors) are modes of variability, or variables describing the state of an Earth System component affecting the troposphere, where the extreme takes place. Predictors act on the troposphere at different timescales: Sub‐days to several days for meteorological drivers (light blue), weeks to ~2 months for land surface drivers (green), weeks to multi‐years for stratospheric drivers (gray), months to decades for