The aims of this study are to quantify the effects of key properties of rainfall time series (frequency, duration, depth, rate and peak, time between events, length of series and precipitation thresholds, among others) on the hydrologic design of sustainable urban drainage systems (SuDS), to test a method for their estimation from daily time series and to quantify their uncertainty. Several typologies of SuDS infrastructures are designed to achieve a target treatment capacity. This target capacity is usually defined according to two methods: treating a percentage of the total volume of rainfall (50, 80, 90, 95, 99%) or treating a percentage of the total number of rainfall events (50, 80, 90, 95, 99%). We considered the city of Madrid as the case study, compiling 58 years of observed data (10-minute time step) and aggregating to daily time series. We obtained the design parameters from the full resolution dataset and then tested a simplified method to estimate them from daily time series of varying length. First, we calculated the design parameters for different storm thresholds (0, 1 and 2 millimeters). Second, we determined the design parameters from the aggregated daily time series by applying a temporal stochastic rainfall generator model (RainSimV3). We estimated the model parameters from daily data and generated 100 series of 58 years at 10-minute time step, and compared the results. Third, we generated 100 series of different lengths (20, 30, 40, 50, 58, 80 and 100 years). Fourth, we generated 100 series of 58 years at 10-minute time step (for each series length). Finally, we analyzed the uncertainty produced by the length of the observed data set. Results showed that, depending on the criteria adopted for the estimation of rainfall design parameters, SuDS structure volumes could vary up to 30 %. Further research includes the analysis of different climate locations.