Optimal water resources management requires hydrological scenarios for the climate situation under consideration. These scenarios may be produced from meteorological scenarios thanks to an appropriate hydrological model. The Laboratory "Hydrology and Land Improvement" (HYDRAM) developed a combined downscaling model for the multisite stochastic generation of the meteorological variables required for the generation of such scenarios. The model combines a statistical downscaling model and a k-nearest neighbour resampling approach to generate hourly precipitation and temperature series from NCEP reanalyses. It was applied for the upper Rhone catchment. Observed statistics are well reproduced for both meteorological variables. Then it was used for the generation of a suite of flood scenarios at different hydrological stations of the studied catchment. The stochastic generator can also be applied to downscale climate experiments from global and/or regional climate models for future climate conditions.