Modelling short-term precipitation extremes with the blended generalised extreme value distribution

A new method is proposed for modelling the yearly maxima of short-term 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 \texttt{R-INLA}. The model is fitted to yearly short-term precipitation maxima from the south of Norway, and is able to quickly produce high-resolution return level maps with uncertainty. Using cross-validation, we find that the proposed model provides a better fit to short-term precipitation data than the standard block maxima model.