  Multiple studies have now demonstrated that machine learning (ML) can give
improved skill for predicting or simulating fairly typical weather events, for
tasks such as short-term and seasonal weather forecasting, downscaling
simulations to higher resolution and emulating and speeding up expensive model
parameterisations. Many of these used ML methods with very high numbers of
parameters, such as neural networks, which are the focus of the discussion
here. Not much attention has been given to the performance of these methods for
extreme event severities of relevance for many critical weather and climate
prediction applications, with return periods of more than a few years. This
leaves a lot of uncertainty about the usefulness of these methods, particularly
for general purpose prediction systems that must perform reliably in extreme
situations. ML models may be expected to struggle to predict extremes due to
there usually being few samples of such events. However, there are some studies
that do indicate that ML models can have reasonable skill for extreme weather,
and that it is not hopeless to use them in situations requiring extrapolation.
This article reviews these studies and argues that this is an area that needs
researching more. Ways to get a better understanding of how well ML models
perform at predicting extreme weather events are discussed.
