  Deep learning models have gained popularity in climate science, following
their success in computer vision and other domains. For instance, researchers
are increasingly employing deep learning techniques for downscaling climate
data, drawing inspiration from image super-resolution models. However, there
are notable differences between image data and climate data. While image data
typically falls within a specific range (e.g., [0, 255]) and exhibits a
relatively uniform or normal distribution, climate data can possess arbitrary
value ranges and highly uneven distributions, such as precipitation data. This
non-uniform distribution presents challenges when attempting to directly apply
existing computer vision models to climate science tasks. Few studies have
addressed this issue thus far. In this study, we explore the effects of loss
functions and non-linear data pre-processing methods for deep learning models
in the context of climate downscaling. We employ a climate downscaling
experiment as an example to evaluate these factors. Our findings reveal that L1
loss and L2 loss perform similarly on some more balanced data like temperature
data while for some imbalanced data like precipitation data, L2 loss performs
significantly better than L1 loss. Additionally, we propose an approach to
automatically learn the non-linear pre-processing function, which further
enhances model accuracy and achieves the best results.
