Skillful seasonal streamflow forecasts are increasingly requested for decision-making in areas such as drought risk assessment or reservoir management. Meteorological forcing can be the major source of uncertainty in seasonal forecasts as early as in the first month of the forecast period. The choice of the hydrological model inputs thus has a major impact on the quality of generated streamflow forecasts. In this study, we assess the impact of two types of precipitation forecast post-treatment: 1) bias correction and 2) conditioning, on streamflow forecast quality.