In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents for the first time an attribution of this precipitation-induced flooding from a combined meteorological and hydrological perspective. Experiments were conducted with three observational data sets and two climate models to estimate changes in extreme 10-day precipitation event frequency over the Brahmaputra basin. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed. In all three observational precipitation data sets the climate change trends for extreme precipitation similar to observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model shows a significant positive influence of anthropogenic climate change, whereas the other simulates a cancellation between the increase due to greenhouse gases and a decrease due to sulphate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than for precipitation, but the 95 % confidence interval still encompasses no change in risk. For the future, all models project an increase in probability of extreme events at 2 °C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation, and about a factor 1.5 more likely for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: We find the change in risk to be greater than one and of similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available, or as an additional measure to confirm qualitative conclusions. Besides, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols.