We present a Stochastic Weather Generator described based on a multisite Hidden Markov Model (HMM) and trained with French weather stations data. It generates correlated precipitation, with a special focus on seasonality and 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. Its architecture allows easy integration of other weather variables. We show an application where the model is trained on future climate scenarios, allowing easy comparison and interpretation with the historical data in terms of parameters evolution and extremes.