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Atmospheric Extreme Events (EEs) cause severe damages to human societies andecosystems. The frequency and intensity of EEs and other associated events areincreasing in the current climate change and global warming risk. The accurateprediction, characterization, and attribution of atmospheric EEs is therefore akey research field, in which many groups are currently working by applyingdifferent methodologies and computational tools. Machine Learning (ML) methodshave arisen in the last years as powerful techniques to tackle many of theproblems related to atmospheric EEs. This paper reviews the ML algorithmsapplied to the analysis, characterization, prediction, and attribution of themost important atmospheric EEs. A summary of the most used ML techniques inthis area, and a comprehensive critical review of literature related to ML inEEs, are provided. A number of examples is discussed and perspectives andoutlooks on the field are drawn.

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review