Multivariate stochastic downscaling for semicontinuous data

The paper proposes a Bayesian hierarchical model to scale down and adjusts deterministic weather model output of temperature and precipitation with meteorological observations, extending the existing literature along different direc-tions. These non-independent data are used jointly into a stochastic model calibra-tion that accounts for the uncertainty in the numerical model. Dependence between temperature and precipitation is introduced through spatial latent processes, at both point and grid cell resolution. Occurrence and accumulation of precipitation are considered through a two-stage spatial model due to the large number of zero mea-surements and the right-skewness of the distribution of positive rainfall amounts. The model is applied to data coming from the Emilia-Romagna region (Italy).