A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs - Shiqi Fang; Reetam Majumder; Emily Hector; Brian Reich; A. Sankarasubramanian (Scientific Data)
Abstract Global Climate Models (GCMs) are essential for climate projections but often exhibit biases, particularly in representing extremes and multivariate dependencies, which limit their utility in impact assessments. Traditional bias correction (BC) methods, such as quantile mapping, address marginal distributions but fail to correct joint extremes and cross-variable relationships. To address these challenges, we propose a Complete Density Correction using Normalizing Flows (CDC-NF), a novel method leveraging invertible transformations to adjust the full joint distribution of GCM outputs. Using observational data from NOAA nClimGrid-daily and CMIP6 GCM projections, The CDC-NF method was applied at a daily temporal resolution to precipitation and maximum temperature outputs from CMIP6 GCM projections. Compared to traditional BC methods, CDC-NF demonstrated substantial improvements in Wasserstein Distance, RMSE, and PBIAS, particularly for the 90th percentile extremes. Additionally, it preserved cross-correlation structure, enhancing reliability in modeling compound extremes. CDC-NF represents a significant advancement in BC, providing a robust framework for addressing GCM biases and improving climate impact studies in a changing climate.