Unsupervised deep learning bias correction of CMIP6 global ensemble precipitation predictions with Cycle Generative Adversarial Network

Abstract

Climate change significantly impacts agricultural production, ecosystem stability, and socioeconomic development. Global Climate Models (GCMs) serve as the primary tool for simulating historical and future precipitation patterns. However, due to issues such as coarse resolution, boundary condition, and parameterization, model outputs require bias correction. With the evolution of deep learning techniques, supervised Convolutional Neural Network (CNN) frameworks have gained popularity in the area of climate model bias correction but face limitations in spatial correlation assumptions and data sparsity, particularly for extreme precipitation This study proposed an unsupervised learning approach using Cycle Generative Adversarial Network (CycleGAN) to correct the ensemble mean bias of models and compare its performance with CNN and Quantile Mapping methods. The results demonstrate that the proposed CycleGAN approach outperforms both CNN and Quantile Mapping in ensemble mean bias correction. It effectively learns the overall distribution of precipitation through an adversarial process and yields better extreme precipitation predictions.