  Future climate change scenarios are usually hypothesized using simulations
from weather generators. However, there only a few works comparing and
evaluating promising deep learning models for weather generation against
classical approaches. This study shows preliminary results making such
evaluations for the multisite precipitation synthesis task. We compared two
open-source weather generators: IBMWeathergen (an extension of the Weathergen
library) and RGeneratePrec, and two deep generative models: GAN and VAE, on a
variety of metrics. Our preliminary results can serve as a guide for improving
the design of deep learning architectures and algorithms for the multisite
precipitation synthesis task.
