The challenges of climate change force industrials to carefully analyze the resilience of their assets to anticipate future weather conditions. In particular, the estimation of future extreme hydrometeorological events, like the frequency of long-lasting dry spells, is critical for hydropower/nuclear generation or agriculture. Stochastic Weather Generators (SWG) are essential tools to determine these future risks, as they can quickly sample climate statistics from models. In this work, the SWG described and validated with French historical data is based on a spatial Hidden Markov Model (HMM). It generates (correlated) multisite precipitation, with a special focus on the correct reproduction of the distribution of dry and wet spells. The hidden states are viewed as global weather regimes, e.g., dry all over France, rainy in the north, etc. The resulting model is fully interpretable; it can even approximately recover large-scale structures such as North Atlantic Oscillations. The model achieves very good performances, specifically in terms of extremes, e.g., drought statistics. Its architecture allows easy integration of multiple weather variables. We show an application where it is used to generate realistic precipitation, temperature, solar radiation and evapotranspiration time series as inputs to a crop model. We'll also show how the model parameters evolve when trained on RCP climate scenarios and the impact of these changes on extreme climatic events.