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.

本研究解决了全球气候模型输出中由于未观察混淆因素导致的系统偏差问题。我们提出了一种新颖的偏差校正方法，利用气候模型和观测数据构建因子模型，以捕捉多因子潜在混淆因素。这种方法显著提高了降水输出的准确性，提供了一种稳健且理论基础扎实的气候模型偏差校正方案。

气候模型偏差校正的去混淆方法