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<p>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.</p>

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A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月23日

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  • Shiqi Fang,
  • Reetam Majumder,
  • Emily Hector,
  • Brian Reich,
  • A. Sankarasubramanian

摘要

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.