The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming. The complex cross-scale interactions of processes that produce precipitation are challenging to model, however, inducing potentially strong biases in ESM fields, especially regarding extremes. State-of-the-art bias correction methods only address errors in the simulated frequency distributions locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here, we show that a post-processing method based on physically constrained generative adversarial networks (cGANs) can correct biases of a state-of-the-art, CMIP6-class ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions equally well as gold-standard bias-adjustment frameworks, it strongly outperforms any existing methods in the correction of spatial patterns, especially in terms of the characteristic spatial intermittency of precipitation extremes.

翻译：地球系统模型（ESMs）中降水的准确表征对于可靠预测人为全球变暖带来的生态和社会经济影响至关重要。然而，产生降水的过程中存在的复杂跨尺度相互作用给建模带来了挑战，导致ESM场中可能存在较强的偏差，尤其是在极端事件方面。最先进的偏差校正方法仅能解决每个独立网格单元局部模拟频率分布中的误差。到目前为止，改善ESM输出的非现实空间模式（这需要空间上下文信息）仍是无法实现的。在此，我们展示了一种基于物理约束生成对抗网络（cGANs）的后处理方法，可以同时校正最先进的CMIP6级ESM在局部频率分布和空间模式中的偏差。尽管我们的方法在改善局部频率分布方面与黄金标准的偏差调整框架表现相当，但在空间模式的校正上，尤其是在降水极端事件特征性空间间歇性的校正方面，它显著优于任何现有方法。