Climate change exacerbates extreme weather events like heavy rainfall and flooding. As these events cause severe socioeconomic damage, accurate high-resolution simulation of precipitation is imperative. However, existing Earth System Models (ESMs) struggle to resolve small-scale dynamics and suffer from biases. Traditional statistical bias correction and downscaling methods fall short in improving spatial structure, while recent deep learning methods lack controllability and suffer from unstable training. Here, we propose a machine learning framework for simultaneous bias correction and downscaling. We first map observational and ESM data to a shared embedding space, where both are unbiased towards each other, and then train a conditional diffusion model to reverse the mapping. Only observational data is used for the training, so that the diffusion model can be employed to correct and downscale any ESM field without need for retraining. Our approach ensures statistical fidelity and preserves spatial patterns larger than a chosen spatial correction scale. We demonstrate that our approach outperforms existing statistical and deep learning methods especially regarding extreme events.

翻译：气候变化加剧了极端天气事件，如强降雨和洪水。由于这些事件会造成严重的社会经济损失，因此必须对降水进行精确的高分辨率模拟。然而，现有的地球系统模式难以解析小尺度动力学过程，且存在系统性偏差。传统的统计偏差校正和降尺度方法在改善空间结构方面存在不足，而近期的深度学习方法则缺乏可控性且训练不稳定。本文提出一种用于同步偏差校正与降尺度的机器学习框架。我们首先将观测数据和ESM数据映射到一个共享的嵌入空间，使两者彼此无偏，然后训练一个条件扩散模型来逆转该映射过程。训练仅使用观测数据，因此该扩散模型可用于校正和降尺度任何ESM场，而无需重新训练。我们的方法确保了统计保真度，并保留了大于选定空间校正尺度的空间格局。实验表明，我们的方法尤其在极端事件方面优于现有的统计和深度学习方法。