  A new method is proposed for modelling the yearly maxima of sub-daily
precipitation, with the aim of producing spatial maps of return level
estimates. Yearly precipitation maxima are modelled using a Bayesian
hierarchical model with a latent Gaussian field, with the blended generalised
extreme value (bGEV) distribution used as a substitute for the more standard
generalised extreme value (GEV) distribution. Inference is made less wasteful
with a novel two-step procedure that performs separate modelling of the scale
parameter of the bGEV distribution using peaks over threshold data. Fast
inference is performed using integrated nested Laplace approximations (INLA)
together with the stochastic partial differential equation (SPDE) approach,
both implemented in R-INLA. Heuristics for improving the numerical stability of
R-INLA with the GEV and bGEV distributions are also presented. The model is
fitted to yearly maxima of sub-daily precipitation from the south of Norway,
and is able to quickly produce high-resolution return level maps with
uncertainty. The proposed two-step procedure provides an improved model fit
over standard inference techniques when modelling the yearly maxima of
sub-daily precipitation with the bGEV distribution.
