  Recently, deep learning has emerged as a promising tool for statistical
downscaling, the set of methods for generating high-resolution climate fields
from coarse low-resolution variables. Nevertheless, their ability to generalize
to climate change conditions remains questionable, mainly due to the
stationarity assumption. We propose deep ensembles as a simple method to
improve the uncertainty quantification of statistical downscaling models. By
better capturing uncertainty, statistical downscaling models allow for superior
planning against extreme weather events, a source of various negative social
and economic impacts. Since no observational future data exists, we rely on a
pseudo reality experiment to assess the suitability of deep ensembles for
quantifying the uncertainty of climate change projections. Deep ensembles allow
for a better risk assessment, highly demanded by sectoral applications to
tackle climate change.
