  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 imposing highly restrictive
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 have seen 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: extensions and improvements of this approach and its
model inference for high-dimensional data; 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.
