This study investigated the effect of lag time on the performance of data-driven models, specifically the adaptive network-based fuzzy inference system (ANFIS), in event-based rainfall–runoff modeling. Rainfall and runoff data for a catchment in Singapore were chosen for this study. For the purpose of this study, lag time was determined from cross-correlation analysis of the rainfall and runoff time series. Rainfall antecedents were the only inputs of the models and direct runoff was the desired output. An ANFIS model with three sub-models defined based on three different ranges of lag times was developed. The performance of the sub-models was compared with previously developed ANFIS models and the physicallybased Storm Water Management Model (SWMM). The ANFIS sub-models gave significantly superior results in terms of the RMSE, r2, CE and the prediction of the peak discharge, compared to other ANFIS models where the lag time was not considered. In addition, the ANFIS sub-models provided results that were comparable with results from SWMM. It is thus concluded that the lag time plays an important role in the selection of events for training and testing of data-driven models in event-based rainfall–runoff modeling.