Stochastic weather generators (SWGs) are statistical models used to study climate variability. We design an interpretable multisite SWG for precipitation, capable of learning large-scale weather regimes solely from French observational data. The model reproduces extreme events like droughts and heavy rain and is applied to climate models under historical and Representative Concentration Pathway (RCP) scenarios. This type of model aims to assess large-scale weather risks, such as those impacting energy systems and agriculture.