Stochastic weather generators are statistical models that simulate random sequences of atmospheric variables such as temperature and rainfall and reproduce the spatial and temporal dynamics and correlation structures. Generated synthetic sequences provide a set of alternate realizations. AWA performed a stochastic weather modeling study based on a multisite stochastic modeling approach using daily observations of precipitation and minimum and maximum temperatures from 35 sites located in the Rocky Mountains in Alberta. Stochastic weather modeling utilized the Multi-site Auto-regressive Weather GENerator (RMAWGEN)) framework. Observed precipitation and temperature records were used to calibrate RMAWGEN model for the 1966–2019 period. Validation was performed on the calibration period data. Results of the calibrated model demonstrate that the model was able to capture spatiotemporal characteristics of observed precipitation and temperature fields present in observed data. This was used to generate 10 iterations of 1,000-years of daily weather sequences of precipitation and minimum and maximum temperatures at each station. The 10 iterations equated to 10,000-years of simulated plausible sequences. Results were used as inputs to a continuous simulation hydrologic model built using the Raven framework, calibrated against 35-years of daily inflow data at 28 hydrometric sites. These simulated runoff to a hydroelectric system comprising 15 dams with a drainage area of 18,500 km2. Outputs were used to assess probabilistic flood risk at the 15 dams, considering a wide range of event parameters and operational decisions. Results will be used in future spillway capacity reviews, dam safety assessments and seasonal flood operation planning.