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：地球系统模型中对降水的精确表征对于可靠预测人为全球变暖引发的生态和社会经济影响至关重要。然而，产生降水的多尺度跨过程相互作用具有建模挑战性，这可能导致地球系统模型场出现潜在强偏差，尤其是在极端事件方面。现有最先进的偏差校正方法仅能解决每个独立网格单元模拟频率分布的局部误差。至今仍无法改善地球系统模型输出中不真实的空间格局——这需要空间上下文信息。本研究表明，基于物理约束的生成对抗网络后处理方法可同时校正CMIP6类最先进地球系统模型在局部频率分布和空间格局上的偏差。尽管我们的方法在改善局部频率分布方面与黄金标准偏差校正框架表现同样出色，但在空间格局校正方面，特别是在降水极端事件特有的空间间歇性特征方面，其性能显著超越现有所有方法。