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A new method is proposed for modelling the yearly maxima of sub-dailyprecipitation, with the aim of producing spatial maps of return levelestimates. Yearly precipitation maxima are modelled using a Bayesianhierarchical model with a latent Gaussian field, with the blended generalisedextreme value (bGEV) distribution used as a substitute for the more standardgeneralised extreme value (GEV) distribution. Inference is made less wastefulwith a novel two-step procedure that performs separate modelling of the scaleparameter of the bGEV distribution using peaks over threshold data. Fastinference 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 ofR-INLA with the GEV and bGEV distributions are also presented. The model isfitted to yearly maxima of sub-daily precipitation from the south of Norway,and is able to quickly produce high-resolution return level maps withuncertainty. The proposed two-step procedure provides an improved model fitover standard inference techniques when modelling the yearly maxima ofsub-daily precipitation with the bGEV distribution.

Modelling sub-daily precipitation extremes with the blended generalised extreme value distribution