For the purpose of overcoming the disadvantages of the traditional data-driven model and implementing the high accuracy rainfall-runoff simulation by data-driven model, a new coupled data-driven model named PEK has been proposed in this paper. The PEK model was developed by coupling partial mutual information based on the input variable selection, novel ensemble backpropagation neural network based on discharge forecasting and K-nearest neighbor based on discharge error forecasting. The PEK model has the following characteristics: (1) The separate IVS strategy and the sliding window accumulative rainfall based on model candidate input vector. These two methods combined with the PMI-based IVS approach ensure the sufficiency and parsimony of the input information and is very important to the construction of the high quality data-driven model; (2) The novel ensemble backpropagation neural network and the methodology of calibration were proposed in this paper. The global optimal number of component networks, topology and network parameters were obtained simultaneously by using the NSGA-II multi-objective optimization algorithm and the early stopping LM algorithm. The combination weights of the component networks were obtained by the AIC-based component network weights assignment approach. The EBPNN model can make a good compromise between simulation accuracy and network complexity; (3) The PEK model implemented multi-step forecasting and high accuracy simulation under the non-updating mode. The forecast period was also increased; (4) The PEK model doesn’t need to compute the catchment state variables and implements continuous simulation only by using the initial discharge. In this paper, the PEK and CLS models were applied in hourly rainfall-runoff simulation in the Chengcun watershed and the results were compared. The simulation results indicate that the PEK model is easy to use, is better than CLS in simulation accuracy, can realize multi-step high accuracy simulation and increase the forecasting period of the data-driven model.