Extreme events, such as heat waves, droughts, wildfires, floods, landslides, tornadoes, and hurricanes, have significant social, ecological, and technical impacts. Our ability to fully understand and manage the earth system’s extreme events is usually hindered by the lack of characterization data and reliable modeling of these extremes. We seek contributions developing novel scientific machine learning (ML) approaches to enable transformational advances in the efficiency and effectiveness of predicting and managing earth system extremes by automatically learning multiphysics and multiscale processes based on observational or simulation data and extracting meaningful metrics for making decisions, which include (1) data engineering to make extreme events information findable, accessible, interoperable, and reusable (FAIR) for ML; (2) exploratory data analysis, pattern recognition, and signature discovery for extreme event understanding; (3) system complexity reduction or identification of influential drivers of extremes; and (4) physics-informed ML and ML-guided numerical modeling, experimental design, and decision-making.