Our Research
Anthropogenic climate change is driving an increase in the frequency, intensity, and complexity of extreme weather events, particularly tropical cyclones and compound climate extremes. Our research uses high-resolution, physics-based simulations and data-driven machine learning models to better understand how hurricanes—through storm surge, extreme rainfall, and their compound interactions—impact coastal regions under both current and future climate conditions. By modeling synthetic hurricanes and simulating flood hazards in various cities, we find that the recurrence interval of destructive compound flooding events may shrink dramatically by the end of the century. These insights are critical for guiding climate adaptation strategies, including the design of resilient infrastructure and the implementation of nature-based solutions to protect urban areas from escalating storm-related damages.
Beyond tropical cyclones, we investigate compound climate extremes—such as the simultaneous occurrence of heatwaves and droughts—that produce amplified impacts on both natural and human systems. Through advanced Bayesian and statistical frameworks, we quantify how the risks of such events are increasing due to human-driven warming. For instance, we find that the likelihood of concurrent warm and dry years has doubled globally, while the risk of compound droughts has risen significantly in the U.S., especially in the West. Our models emphasize the role of causality, short-term memory in climate extremes, and nonstationary climate dynamics, offering vital tools for sectors like agriculture, water resource management, and energy. By combining physical modeling with machine learning and supervised dimensionality reduction, we also advance predictive capabilities for hydro-climate extremes, enabling earlier and more accurate warnings. Together, this integrated approach offers a robust foundation for understanding multidimensional climate risks and building climate resilience in an increasingly volatile world.