  We present the methods employed by team `Uniofbathtopia' as part of the Data
Challenge organised for the 13th International Conference on Extreme Value
Analysis (EVA2023), including our winning entry for the third sub-challenge.
Our approaches unite ideas from extreme value theory, which provides a
statistical framework for the estimation of probabilities/return levels
associated with rare events, with techniques from unsupervised statistical
learning, such as clustering and support identification. The methods are
demonstrated on the data provided for the Data Challenge -- environmental data
sampled from the fantasy country of `Utopia' -- but the underlying assumptions
and frameworks should apply in more general settings and applications.
