  Projection of changes in extreme indices of climate variables such as
temperature and precipitation are critical to assess the potential impacts of
climate change on human-made and natural systems, including critical
infrastructures and ecosystems. While impact assessment and adaptation planning
rely on high-resolution projections (typically in the order of a few
kilometers), state-of-the-art Earth System Models (ESMs) are available at
spatial resolutions of few hundreds of kilometers. Current solutions to obtain
high-resolution projections of ESMs include downscaling approaches that
consider the information at a coarse-scale to make predictions at local scales.
Complex and non-linear interdependence among local climate variables (e.g.,
temperature and precipitation) and large-scale predictors (e.g., pressure
fields) motivate the use of neural network-based super-resolution
architectures. In this work, we present auxiliary variables informed
spatio-temporal neural architecture for statistical downscaling. The current
study performs daily downscaling of precipitation variable from an ESM output
at 1.15 degrees (~115 km) to 0.25 degrees (25 km) over the world's most
climatically diversified country, India. We showcase significant improvement
gain against three popular state-of-the-art baselines with a better ability to
predict extreme events. To facilitate reproducible research, we make available
all the codes, processed datasets, and trained models in the public domain.
