  Inference on the extremal behaviour of spatial aggregates of precipitation is
important for quantifying river flood risk. There are two classes of previous
approach, with one failing to ensure self-consistency in inference across
different regions of aggregation and the other requiring highly inflexible
marginal and spatial dependence structure assumptions. To overcome these
issues, we propose a model for high-resolution precipitation data, from which
we can simulate realistic fields and explore the behaviour of spatial
aggregates. Recent developments in spatial extremes literature have seen
promising progress with spatial extensions of the Heffernan and Tawn (2004)
model for conditional multivariate extremes, which can handle a wide range of
dependence structures. Our contribution is twofold: new parametric forms for
the dependence parameters of this model; and a novel framework for deriving
aggregates addressing edge effects and sub-regions without rain. We apply our
modelling approach to gridded East-Anglia, UK precipitation data. Return-level
curves for spatial aggregates over different regions of various sizes are
estimated and shown to fit very well to the data.
