Environmental hazards threaten human health and well-being, both at present and under future climate change. The increasingly widespread use of empirical causal inference methods and large Earth system datasets provides unprecedented opportunities for understanding the connections between climate hazards and human outcomes such as health, economic growth, food security, and more. This session welcomes interdisciplinary research using empirical methods to understand the human impacts of exposure to extreme climate or environmental conditions, broadly defined. We welcome work that uses regression, machine learning, or other data-driven techniques to infer causal effects of environmental exposure on human outcomes; work that attributes changes in human outcomes to anthropogenic climate change; and work that empirically evaluates the potential for adaptation interventions to reduce these impacts. We would be excited to have contributions from a wide variety of disciplines, including Earth system science, economics, environmental health, and more.