Deep Learning has recently emerged as a perfect prognosis downscaling
technique to compute high-resolution fields from large-scale coarse atmospheric
data. Despite their promising results to reproduce the observed local
variability, they are based on the estimation of independent distributions at
each location, which leads to deficient spatial structures, especially when
downscaling precipitation. This study proposes the use of generative models to
improve the spatial consistency of the high-resolution fields, very demanded by
some sectoral applications (e.g., hydrology) to tackle climate change.