  To mitigate the risk posed by extreme rainfall events, we require statistical
models that reliably capture extremes in continuous space with dependence.
However, assuming a stationary dependence structure in such models is often
erroneous, particularly over large geographical domains. Furthermore, there are
limitations on the ability to fit existing models, such as max-stable
processes, to a large number of locations. To address these modelling
challenges, we present a regionalisation method that partitions stations into
regions of similar extremal dependence using clustering. To demonstrate our
regionalisation approach, we consider a study region of Australia and discuss
the results with respect to known climate and topographic features. To
visualise and evaluate the effectiveness of the partitioning, we fit max-stable
models to each of the regions. This work serves as a prelude to how one might
consider undertaking a project where spatial dependence is non-stationary and
is modelled on a large geographical scale.
