Multivariate Stochastic Downscaling for Semicontinuous Data

The paper proposes a Bayesian hierarchical model to scale down and\r\nadjust deterministic weather model output of temperature and precipitation with\r\nmeteorological observations, extending the existing literature along different directions.\r\nThese non-independent data are used jointly into a stochastic calibration model\r\nthat accounts for the uncertainty in the numerical model. Dependence between temperature\r\nand precipitation is introduced through spatial latent processes, at both point\r\nand grid cell resolution. Occurrence and accumulation of precipitation are considered\r\nthrough a two-stage spatial model due to the large number of zero measurements\r\nand the right-skewness of the distribution of positive rainfall amounts. The model is\r\napplied to data coming from the Emilia-Romagna region (Italy).