  Extreme weather events have an enormous impact on society and are expected to
become more frequent and severe with climate change. In this context,
resilience planning becomes crucial for risk mitigation and coping with these
extreme events. Machine learning techniques can play a critical role in
resilience planning through the generation of realistic extreme weather event
scenarios that can be used to evaluate possible mitigation actions. This paper
proposes a modular framework that relies on interchangeable components to
produce extreme weather event scenarios. We discuss possible alternatives for
each of the components and show initial results comparing two approaches on the
task of generating precipitation scenarios.
