This study integrates Xinanjiang (XAJ) and Temporal Convolutional Network - Gated Recurrent Unit (TCN-GRU) via Random Forest (RF) for streamflow simulation. It combines XAJ’s physical modeling with TCN-GRU’s temporal analysis. Validated in four hydrologically diverse basins, the model achieves Nash-Sutcliffe Efficiency (NSE) 0.971–0.991, outperforming traditional models. Robust in flood/interval simulations, analysis identifies dew point temperature and evaporation as key factors through three interpretable methods.