The present invention provides a long-term streamflow forecast method and system based on process-data synergic drive. The long-term streamflow forecast method includes: step 1, collecting data; step 2, constructing a VIC model; step 3, performing interpolation, bias correction, and disaggregation to obtain daily data; step 4, by using climate model forecasts, driving the VIC model; step 5, constructing an improved VIC model by selecting grid cells from the distributed hydrological model, among which the series of monthly soil moisture forecasts in the third layer in all grid cells are treated as independent variables and the monthly streamflow of the outlet hydrological station of the studied watershed is treated as the dependent variables; step 6, driving the improved VIC model to perform the gridded runoff yield calculation of a full time period; step 7, forming a candidate predictor set; step 8, screening predictors and training a deep learning model to obtain a composite model, and performing long-term streamflow forecast.