  This study develops a neural network-based approach for emulating
high-resolution modeled precipitation data with comparable statistical
properties but at greatly reduced computational cost. The key idea is to use
combination of low- and high- resolution simulations to train a neural network
to map from the former to the latter. Specifically, we define two types of
CNNs, one that stacks variables directly and one that encodes each variable
before stacking, and we train each CNN type both with a conventional loss
function, such as mean square error (MSE), and with a conditional generative
adversarial network (CGAN), for a total of four CNN variants. We compare the
four new CNN-derived high-resolution precipitation results with precipitation
generated from original high resolution simulations, a bilinear interpolater
and the state-of-the-art CNN-based super-resolution (SR) technique. Results
show that the SR technique produces results similar to those of the bilinear
interpolator with smoother spatial and temporal distributions and smaller data
variabilities and extremes than the original high resolution simulations. While
the new CNNs trained by MSE generate better results over some regions than the
interpolator and SR technique do, their predictions are still not as close as
the original high resolution simulations. The CNNs trained by CGAN generate
more realistic and physically reasonable results, better capturing not only
data variability in time and space but also extremes such as intense and
long-lasting storms. The new proposed CNN-based downscaling approach can
downscale precipitation from 50~km to 12~km in 14~min for 30~years once the
network is trained (training takes 4~hours using 1~GPU), while the conventional
dynamical downscaling would take 1~month using 600 CPU cores to generate
simulations at the resolution of 12~km over contiguous United States.
