Rainfall–runoff modeling is a complex nonlinear time-series problem in the field of hydrology. Various methods, such as physical-driven and data-driven models, have been developed to study the highly random rainfall–runoff process. In the past 2 years, with the advancement of computing hardware resources and algorithms, deep-learning methods, such as temporal convolutional network (TCN), have been shown to be good prospects in time-series prediction tasks. The aim of this study is to develop a prediction model based on TCN structure to simulate the hourly rainfall–runoff relationship. We use two datasets in the Jingle and Kuye watersheds to test the model under different network structures and compare with the other four models. The results show that the TCN model outperforms the Excess Infiltration and Excess Storage Model (EIESM), artificial neural network, and long short-term memory and improves the flood forecasting accuracy at different foreseeable periods. It is shown that the TCN has a faster convergence rate and is an effective method for hydrological forecasting. HIGHLIGHTS Propose a TCN model for flood simulation and forecast.; Explore the optimal hyperparameter combination of TCN at different forecast periods in flood forecast.; Compare the performance of the models including physical and neural network models.; Apply the TCN model in different watersheds to evaluate the practicality.;