  Deep learning has emerged as a promising tool for precipitation downscaling.
However, current models rely on likelihood-based loss functions to properly
model the precipitation distribution, leading to spatially inconsistent
projections when sampling. This work explores a novel approach by fusing the
strengths of likelihood-based and adversarial losses used in generative models.
As a result, we propose a likelihood-based generative approach for
precipitation downscaling, leveraging the benefits of both methods.
