  Deep generative models are increasingly used to gain insights in the
geospatial data domain, e.g., for climate data. However, most existing
approaches work with temporal snapshots or assume 1D time-series; few are able
to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems
data often exhibit highly irregular and complex patterns, for example caused by
extreme weather events. Because of climate change, these phenomena are only
increasing in frequency. Here, we proposed a novel GAN-based approach for
generating spatio-temporal weather patterns conditioned on detected extreme
events. Our approach augments GAN generator and discriminator with an encoded
extreme weather event segmentation mask. These segmentation masks can be
created from raw input using existing event detection frameworks. As such, our
approach is highly modular and can be combined with custom GAN architectures.
We highlight the applicability of our proposed approach in experiments with
real-world surface radiation and zonal wind data.
