In this study, a novel temporal convolutional neural network (TCNN) model is developed for long-term streamflow projection in California within the Catchment Attributes for Large-Sample Studies (CAMELS) watershed regions. The TCNN model consists of several convolution blocks and causal convolution is used as physical constraint. The ensemble performance of the model is first compared with other machine learning models for streamflow prediction. The model is further assessed through comparison with reduced models and using different hyperparameters, with results suggesting that this model correctly ascertains the physical relationship between input variables and streamflow. The stability of the model and its behavior in the extrapolated regime is assessed through an idealized extreme test with quadruple precipitation and 5 degree C higher temperature. Future streamflow projections are then developed using daily high-resolution Localized Constructed Analogs dataset (LOCA). To understand the importance of the nonlinear machine learning approach, we estimate the degree of nonlinearity in the streamflow response among input variables. Our work shows the ability and potential for TCNNs to perform future hydrology projections.