In rainfall forecasting, selection of an appropriate gauging station to forecast and identification of the appropriate explanatory variables are nontrivial tasks due to the complexity of the physical processes involved and spatiotemporal variability. In this study, for the first time, the concept of transfer entropy is coupled with the complex network analysis for rainfall forecasting by determining the nonlinear directional relationship between the stations. The proposed methodology involves determining the directional relationship between the stations in a given basin, and defining the most influenced station by using the node strength and directed clustering coefficient that are specifically developed for directed-weighted networks. Further, the obtained information flow within the basin is utilized to produce current monthly forecasts of the most influenced station. The methodology is implemented for rainfall forecasting in the Western Mediterranean Basin, Turkey, considering monthly total rainfall data from seven stations. The results indicate that the proposed methodology is useful and effective in the identification of the appropriate station to forecast and the relevant explanatory variables to serve as inputs for the artificial neural networks (ANNs) model. The results from the proposed methodology are compared with those from two widely employed input determination approaches (i.e. using rainfall from the most-correlated station in the basin and using rainfall from all the other stations). It is found that the proposed methodology significantly improves the forecasting performance of the ANN model. The results obtained in this study have broad implications for designing optimal rain gauge density, identification of the complexity of the rain gauge network structure, and interpolation (or extrapolation) of hydrological data for ungauged locations.