To extend the lead time and improve the accuracy of medium-to-long-term runoff forecast, teleconnection factors were incorporated as physical determinants of runoff formation alongside antecedent precipitation-runoff relationships. An RF-Informer model for monthly runoff forecast was developed by integrating the random forest(RF) model for predictor selection with the Informer model, which has demonstrated superior performance in longsequence timeseries forecasting. The proposed model was applied to forecast monthly inflow runoff of the Lianghekou, Jinxi, and Ertan reservoirs in the Yalong River Basin. Results indicate that the integration of teleconnection factors effectively extends the lead time while enhancing the accuracy for forecasting basinscale monthly runoff. By resolving nonlinear couplings between predictors that linear methods overlook, the RFdriven feature selection advances the physical interpretability and predictive skill of runoff forecast; compared with the RFLSTM, RFSVM, and RFBP neural network models, the RFInformer model achieves the minimum forecast error and highest precision in monthly runoff forecast.