Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation of complex climate phenomena. Traditional bias correction methods, which rely on historical observation data and statistical techniques, often neglect unobserved confounders, leading to biased results. This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders. Inspired by recent advances in causality based time series deconfounding, our method first constructs a factor model to learn latent confounders from historical data and then applies them to enhance the bias correction process using advanced time series forecasting models. The experimental results demonstrate significant improvements in the accuracy of precipitation outputs. By addressing unobserved confounders, our approach offers a robust and theoretically grounded solution for climate model bias correction.

翻译：全球气候模型通过模拟地球系统，对预测未来气候变化至关重要。然而，由于模型不确定性、参数化简化以及对复杂气候现象表征不足，GCM输出存在系统性偏差。传统的偏差校正方法依赖历史观测数据和统计技术，往往忽略未观测的混淆因子，导致结果存在偏差。本文提出一种新颖的偏差校正方法，利用GCM和观测数据共同学习一个捕捉多成因潜在混淆因子的因子模型。受近期基于因果关系的时序解混淆进展启发，我们的方法首先构建因子模型从历史数据中学习潜在混淆因子，随后将其应用于增强的偏差校正过程，该过程采用先进的时序预测模型。实验结果表明，该方法显著提高了降水输出的准确性。通过处理未观测的混淆因子，我们的方法为气候模型偏差校正提供了一个稳健且具有理论依据的解决方案。