  Accurate stochastic simulations of hourly precipitation are needed for impact
studies at local spatial scales. Statistically, hourly precipitation data
represent a difficult challenge. They are non-negative, skewed, heavy tailed,
contain a lot of zeros (dry hours) and they have complex temporal structures
(e.g., long persistence of dry episodes). Inspired by frailty-contagion
approaches used in finance and insurance, we propose a multi-site precipitation
simulator that, given appropriate regional atmospheric variables, can
simultaneously handle dry events and heavy rainfall periods. One advantage of
our model is its conceptual simplicity in its dynamical structure. In
particular, the temporal variability is represented by a common factor based on
a few classical atmospheric covariates like temperatures, pressures and others.
Our inference approach is tested on simulated data and applied on measurements
made in the northern part of French Brittany.
