  Weather extremes are a major societal and economic hazard, claiming thousands
of lives and causing billions of dollars in damage every year. Under climate
change, their impact and intensity are expected to worsen significantly.
Unfortunately, general circulation models (GCMs), which are currently the
primary tool for climate projections, cannot characterize weather extremes
accurately. To address this, we present a multi-resolution deep-learning
framework that, firstly, corrects a GCM's biases by matching low-order and tail
statistics of its output with observations at coarse scales; and secondly,
increases the level of detail of the debiased GCM output by reconstructing the
finer scales as a function of the coarse scales. We use the proposed framework
to generate statistically realistic realizations of the climate over Western
Europe from a simple GCM corrected using observational atmospheric reanalysis.
We also discuss implications for probabilistic risk assessment of natural
disasters in a changing climate.
