  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.
