  Precipitation governs Earth's hydroclimate, and its daily spatiotemporal
fluctuations have major socioeconomic effects. Advances in Numerical weather
prediction (NWP) have been measured by the improvement of forecasts for various
physical fields such as temperature and pressure; however, large biases exist
in precipitation prediction. We augment the output of the well-known NWP model
CFSv2 with deep learning to create a hybrid model that improves short-range
global precipitation at 1-, 2-, and 3-day lead times. To hybridise, we address
the sphericity of the global data by using modified DLWP-CS architecture which
transforms all the fields to cubed-sphere projection. Dynamical model
precipitation and surface temperature outputs are fed into a modified DLWP-CS
(UNET) to forecast ground truth precipitation. While CFSv2's average bias is +5
to +7 mm/day over land, the multivariate deep learning model decreases it to
within -1 to +1 mm/day. Hurricane Katrina in 2005, Hurricane Ivan in 2004,
China floods in 2010, India floods in 2005, and Myanmar storm Nargis in 2008
are used to confirm the substantial enhancement in the skill for the hybrid
dynamical-deep learning model. CFSv2 typically shows a moderate to large bias
in the spatial pattern and overestimates the precipitation at short-range time
scales. The proposed deep learning augmented NWP model can address these biases
and vastly improve the spatial pattern and magnitude of predicted
precipitation. Deep learning enhanced CFSv2 reduces mean bias by 8x over
important land regions for 1 day lead compared to CFSv2. The spatio-temporal
deep learning system opens pathways to further the precision and accuracy in
global short-range precipitation forecasts.
