Data Assimilation Research Team Events / Media

Prof. Hyun-Han Kwon (University of Seoul)

Bayesian Mixture Extreme-Value Modeling of Nonstationary Extreme Precipitation Across U.S. Regions

Extreme precipitation is a major driver of flood risk, infrastructure stress, and climate-related disaster losses. However, annual maximum rainfall often reflects multiple physical mechanisms, including frontal or convective systems, tropical-cyclone-related rainfall, and transitional atmospheric regimes. Treating these extremes as samples from a single homogeneous process can obscure how regional rainfall risks are changing. This talk presents an ongoing study of nonstationary extreme precipitation using a Bayesian mixture extreme-value model. The model represents annual maximum daily precipitation as a combination of latent low- and high-intensity rainfall regimes, with time-varying component behavior and regime probabilities. This allows changes in return levels to be separated into contributions from baseline rainfall intensity, high-intensity event magnitude, and the probability of entering an extreme-producing regime. The framework is applied to long-term U.S. station records across the Southeast/Gulf, Mid-Atlantic, Northeast, and inland-control regions. Tropical-cyclone proximity and ERA5-based atmospheric diagnostics are used as external physical evidence, rather than as imposed predictors in the likelihood, to interpret the latent high-intensity regime and its regional variability. The broader goal is to move extreme-value analysis beyond stationary design estimation toward mechanism-aware and decision-relevant understanding of climate risk. By linking Bayesian uncertainty quantification, hydrometeorological interpretation, and regional comparison, this work provides a basis for improved infrastructure planning, impact-based forecasting, and future AI-enabled climate risk services