Monthly streamflow forecasting is vital for the management of water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In the current study, the feasibility of a relatively new AI model, namely the convolutional neural network (CNN), is explored for forecasting monthly streamflow. The CNN is a method of deep learning, the unique convolution-pooling mechanism in which creates its superior attribute of automatically extracting critical features from input layers. Hydrological and large-scale atmospheric circulation variables including rainfall, streamflow, and atmospheric circulation factors (ACFs) are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The ANN and ELM with inputs identified based on cross-correlation analysis (CC) and mutual information analysis (MI) are established for comparative analysis. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN performs better than ANN and ELM across all the statistical measures. Moreover, CNN shows better stability in forecasting accuracy.