Down-scaling and bias correction of precipitation with generative machine learning models 

Earth system models (ESMs) are crucial for understanding and predicting the behaviour of the Earth’s climate system. Understanding and accurately simulating precipitation is particularly important for assessing the impacts of climate change, predicting extreme weather events, and developing sustainable strategies to manage water resources and mitigate associated risks. However, earth system models are prone to large precipitation biases because the relevant processes occur on a large range of scales and involve substantial uncertainties. In this work, we aim to correct such model biases by training generative machine learning models that map between model data and observational data. We address the challenge that the datasets are not paired, meaning that there is no sample-related ground truth to compare the model output to, due to the chaotic nature of geophysical flows. This challenge renders many machine learning approach unsuitable, and also implies a lack of performance metrics. Our main contribution is the construction of a proxy variable that overcomes this problem and allows for supervised training and evaluation of a bias correction model. We show that a generative model is then able to correct spatial patterns and remove statistical biases in the South American domain. The approach successfully preserves large scale structures in the climate model fields while correcting small scale biases in the model data’s spatio-temporal structure and frequency distribution.