Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations. This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.

翻译：全球气候模型的预测通常由于气候模拟的巨大计算成本而在粗空间尺度上运行。这引发了人们对统计降尺度方法的极大兴趣，该过程类似于计算机视觉领域的超分辨率技术，旨在提供更局部的区域气候信息。在本研究中，我们将条件归一化流应用于气候变量降尺度任务。通过在ERA5水含量数据集上对不同上采样因子进行实验，我们展示了该方法优异的性能表现。此外，我们证明该方法能够通过拟合条件分布均值的标准差来评估预测不确定性。