  The formation of precipitation in state-of-the-art weather and climate models
is an important process. The understanding of its relationship with other
variables can lead to endless benefits, particularly for the world's monsoon
regions dependent on rainfall as a support for livelihood. Various factors play
a crucial role in the formation of rainfall, and those physical processes are
leading to significant biases in the operational weather forecasts. We use the
UNET architecture of a deep convolutional neural network with residual learning
as a proof of concept to learn global data-driven models of precipitation. The
models are trained on reanalysis datasets projected on the cubed-sphere
projection to minimize errors due to spherical distortion. The results are
compared with the operational dynamical model used by the India Meteorological
Department. The theoretical deep learning-based model shows doubling of the
grid point, as well as area averaged skill measured in Pearson correlation
coefficients relative to operational system. This study is a proof-of-concept
showing that residual learning-based UNET can unravel physical relationships to
target precipitation, and those physical constraints can be used in the
dynamical operational models towards improved precipitation forecasts. Our
results pave the way for the development of online, hybrid models in the
future.
