Recent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes’s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.