  Machine learning models have been employed to perform either physics-free
data-driven or hybrid dynamical downscaling of climate data. Most of these
implementations operate over relatively small downscaling factors because of
the challenge of recovering fine-scale information from coarse data. This
limits their compatibility with many global climate model outputs, often
available between $\sim$50--100 km resolution, to scales of interest such as
cloud resolving or urban scales. This study systematically examines the
capability of convolutional neural networks (CNNs) to downscale surface wind
speed data over land surface from different coarse resolutions (25 km, 48 km,
and 100 km resolution) to 3 km. For each downscaling factor, we consider three
CNN configurations that generate super-resolved predictions of fine-scale wind
speed, which take between 1 to 3 input fields: coarse wind speed, fine-scale
topography, and diurnal cycle. In addition to fine-scale wind speeds,
probability density function parameters are generated, through which sample
wind speeds can be generated accounting for the intrinsic stochasticity of wind
speed. For generalizability assessment, CNN models are tested on regions with
different topography and climate that are unseen during training. The
evaluation of super-resolved predictions focuses on subgrid-scale variability
and the recovery of extremes. Models with coarse wind and fine topography as
inputs exhibit the best performance compared with other model configurations,
operating across the same downscaling factor. Our diurnal cycle encoding
results in lower out-of-sample generalizability compared with other input
configurations.
