This study demonstrates, in the context of flood frequency analysis, the potential of a recently proposed hierarchical Bayesian approach to combine information from multiple models. The approach explicitly accommodates shared multi-model discrepancy as well as the probabilistic nature of the flood estimates, and treats the available models as a sample from a hypothetical complete (but unobserved) set of models. The methodology is applied to flood estimates from multiple hydrological projections (the Future Flows Hydrology dataset) for 135 catchments in the UK. The advantages of the approach are shown to be: 1) to ensure adequate ‘baseline' with which to compare future changes; 2) to reduce flood estimate uncertainty; 3) to maximise use of statistical information in circumstances where multiple weak predictions individually lack power, but collectively provide meaningful information; 4) to diminish the importance of model consistency when model biases are large; and 5) to explicitly consider the influence of the (model performance) stationarity assumption. Moreover, the analysis indicates that reducing shared model discrepancy is the key to further reduction of uncertainty in the flood frequency analysis. The findings are of value regarding how conclusions about changing exposure to flooding are drawn, and to flood frequency change attribution studies. This article is protected by copyright. All rights reserved.