  Intense precipitation events are commonly known to be associated with an
increased risk of flooding. As a result of the societal and infrastructural
risks linked with flooding, extremes of precipitation require careful
modelling. Extreme value analysis is typically used to model large
precipitation events, though a site-by-site analysis tends to produce spatially
inconsistent risk estimates. In reality, one would expect neighbouring
locations to have more similar risk estimates than locations separated by large
distances. We present an approach, in the Bayesian hierarchical modelling
framework, that imposes a spatial structure on the parameters of a generalised
Pareto distribution. In particular, we look at the clear benefits of this
approach in improving spatial consistency of return level estimates and
increasing precision of these estimates. Unlike many previous approaches that
pool information over locations, we account for the spatial dependence of the
data in our confidence intervals. We implement this model for gridded
precipitation data over Great Britain.
