  Addressing the challenges of climate change requires accurate and
high-resolution mapping of geospatial data, especially climate and weather
variables. However, many existing geospatial datasets, such as the gridded
outputs of the state-of-the-art numerical climate models (e.g., general
circulation models), are only available at very coarse spatial resolutions due
to the model complexity and extremely high computational demand.
Deep-learning-based methods, particularly generative adversarial networks
(GANs) and their variants, have proved effective for refining natural images
and have shown great promise in improving geospatial datasets. This paper
describes a conditional GAN-based stochastic geospatial downscaling method that
can accommodates very high scaling factors. Compared to most existing methods,
the method can generate high-resolution accurate climate datasets from very
low-resolution inputs. More importantly, the method explicitly considers the
uncertainty inherent to the downscaling process that tends to be ignored in
existing methods. Given an input, the method can produce a multitude of
plausible high-resolution samples instead of one single deterministic result.
These samples allow for an empirical exploration and inferences of model
uncertainty and robustness. With a case study of gridded climate datasets (wind
velocity and solar irradiance), we demonstrate the performances of the
framework in downscaling tasks with large scaling factors (up to $64\times$)
and highlight the advantages of the framework with a comprehensive comparison
with commonly used and most recent downscaling methods, including area-to-point
(ATP) kriging, deep image prior (DIP), enhanced super-resolution generative
adversarial networks (ESRGAN), physics-informed resolution-enhancing GAN (PhIRE
GAN), and an efficient diffusion model for remote sensing image
super-resolution (EDiffSR).
