The understanding of climate change and its impacts gained the attention of many researchers, especially the impacts related to urban drainage and stormwater systems. Flood studies are therefore becoming increasingly important. However, these studies require high temporal resolution of rainfall data mainly on an hourly or sub-hourly scale. Many meteorological stations provide longer daily rainfall data as compared to hourly data, also there are less stations providing hourly data. Furthermore, global circulation model’s outputs have lower temporal resolution than required in impact assessment studies. Therefore, using disaggregation tools became necessary to deal with this issue, as they provide higher resolution of rainfall data which aggregates up to the coarser scale data. In this study, the disaggregation method based on the random Bartle-Lewis model is tested on rainfall data of Dresden (Germany). The daily historical rainfall records are disaggregated into hourly data and then compared with the historical ones. In this study, both hourly rainfall data (historical and disaggregated) are input into a rainfall-runoff model (e.g. SWMM model) to evaluate the flooding of an urban drainage subnetwork in both cases. The study shows the good performance of the model in preserving statistical characteristics of the rainfall data. However, the disaggregated hourly data doesn’t coincide with the real historical hourly data. Some rainfall events were selected for the simulation of the flooding of the urban drainage subnetwork using both disaggregated and real historical hourly data. Results have showed that the flooding volumes were close to a great extent in both cases, number of flooding nodes was also approached, but in some cases underestimated. Furthermore, the time of flooding occurrence was different due to the shift in the time of rainfall events occurrence simulated by the disaggregation tool. However, using the disaggregation tool’s results would still provide an overview about the urban flooding situation, and could be especially used for the disaggregation of GCMs output which will serve in climate change impacts studies, namely on urban drainage networks.