A spatio-temporal weather generator for the temperature over France

Abstract. Stochastic weather generators are efficient statistical models producing synthetic weather series by replicating key statistical properties without the computational cost of physical models. However, for applications requiring temperature simulation over a large area, challenges arise due to non-stationarity over time and spatial-temporal dependencies. This paper introduces a daily stochastic weather generator for temperature with arbitrary spatial resolution. The non-stationarity issue is addressed using a decomposition method to separate deterministic terms (trends and seasonality), from the stochastic part representing the underlying climate variability. We extend the existing local decomposition method to extrapolate to any point in space. The spatial-temporal dependence is modeled through a Gaussian field with a non-separable covariance function, accommodating complex interactions between time and space. Our generator, calibrated on a few French weather stations, is validated using several spatio-temporal indicators. First, we evaluate the generator's performance at the fitting stations, comparing simulated and observed indicators. Subsequently, we compare our spatial simulations to a high resolution gridded observation dataset. Results demonstrate that the proposed generator accurately captures the observed spatio-temporal statistics, even for extreme events such as large scale persistent heat waves.