Abstract. Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall-runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses recently introduced, conceptually simple reservoir computing models for one-day-ahead forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent models. Two modifications on the reservoir computing models are made to increase the hydrologically relevant information content of their internal state. The results show that the reservoir computing networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that reservoir computing models can be considered promising alternatives to traditional artificial neural networks in rainfall-runoff modelling.