Abstract. Flooding is a frequent natural hazard in the Brahmaputra basin during the South Asian summer monsoon. Understanding the causes of flood severity is essential for flood management decisions, but to date there has been little attempt to identify sub-seasonal variability of flood characteristics and drivers for the Brahmaputra in Bangladesh. In the 2017 summer monsoon, there was severe flooding in Bangladesh, but the Brahmaputra River, as well as its tributaries, behaved unusually compared to previous major flood events. This study analyses different hydrometeorological drivers of these floods, providing valuable information for the assessment and forecasting of future flood events. Water level and river flow time series have been decomposed using wavelet analysis to study the temporal variability within the hydrological cycle. During the 2017 monsoon, the extreme rainfall in August caused the water level of the Brahmaputra river and its tributaries to rise rapidly and exceed their previous historical record. This heavy rainfall was associated with a northward shift of the monsoon trough, creating active monsoon conditions in the Brahmaputra basin. The rainfall was localised over the lower sub-basins adjacent to the northern border of Bangladesh. The estimated river discharge in 2017 was slightly lower than the two previous major flood events in 1998 and 1988. The wavelet analysis of both daily water level and discharge shows that a high frequency component drove the severe flooding in 2017, compared to the low frequency component in 1998, where widespread basin accumulated rainfall acted as main driver of the flooding. The study concludes that the location and magnitude of extreme rainfall are key drivers controlling on the characteristics of the Brahmaputra floods. Understanding these drivers is essential for flood forecasting, in order to predict the timing, magnitude and duration of flooding, and also for understanding future climate change impacts on flooding. The study recommendations include analysing the synoptic situation along with different intra-seasonal oscillations as well as considering the spatial location of rainfall events for flood forecasting.