Extreme events such as heat waves and wildfire outbreaks can threaten lives, property, ecosystems, infrastructure, and economic activity. These events often occur quite suddenly, are clustered in space, and arise from complicated interactions among weather, ecosystem, land, and ocean processes. Current forecasting methods can have difficulty detecting when ordinary conditions may rapidly develop into damaging extremes, especially when multiple hidden drivers interact over space and time. This project develops new statistical and deep learning modeling and computational tools to improve understanding and prediction of such events. By linking data-driven forecasting with scientific knowledge about how dynamic systems grow and interact, the work seeks to provide earlier warning of hazards that affect public safety, emergency preparedness, agriculture, energy systems, water resources, and community resilience. The project also advances the national interest by strengthening the mathematical, statistical, machine learning, and artificial intelligence foundations needed to anticipate high-impact risks, producing open-source software for use by other researchers and practitioners, and training students with multidisciplinary expertise spanning statistics, dynamical systems, and artificial intelligence. The resulting methods and tools may also benefit other fields in which rare but extreme events occur, including neuroscience, cardiology, economics, and national security applications