This manuscript presents a simple yet effective method to account for uncertainty in hydrologic ensemble forecasting applications. Most operational hydrological ensemble forecasting systems only account for uncertainty in future climate (e.g. precipitation) forcings, ignoring other sources of uncertainty (e.g. model error). The result can be under-dispersive and overconfident forecasts. Ensemble dressing is a form of statistical post-processing to include information about the uncertainty of individual ensemble members. First, the historical simulations can be corrected to remove systematic biases. Next, the simulated and observed flow data are transformed to ensure that model residuals can be fitted to a distribution (e.g. normal); this study uses a 2-parameter log-sinh transformation. Model residuals in transformed space determine the width of the error distribution. Ensemble forecasts are then generated and transformed. Each ensemble is dressed with the error distribution, the results are untransformed, and finally, a probabilistic forecast is derived from the collective distribution of the dressed ensembles. The method is applied to 128 catchments in southeast Australia, demonstrating that the raw ensemble can be made reliable through the use of this dressing method. Copyright © 2012 John Wiley & Sons, Ltd.