[Objectives] To synergize the strengths of process-driven and data-driven hydrological models, this study develops a short- to medium-term runoff forecasting method that integrates meteorological and hydrological forecasting information via a deep learning architecture. The proposed methodology is implemented and validated in the Yuanjiang River basin, focusing on four large‑scale cascade reservoirs—Sanbanxi, Baishi, Tuokou, and Wuqiangxi. [Methods] The retrospective precipitation and temperature forecasts issued by the European Centre for Medium‑Range Weather Forecasts (ECMWF) are systematically evaluated and bias‑corrected using statistical post‑processing techniques, so as to enhance the reliability of meteorological forcing. These corrected products are then used as inputs to drive two widely adopted conceptual rainfall‑runoff models, namely GR4J and the Xinanjiang model, which were calibrated using observed meteorological and hydrological datasets. Two rainfall‑runoff models represent distinct hydrological processes with different structural complexities and the number of free parameters. Concurrently, a set of intermediate process‑related variables that exhibit strong correlations with observed runoff—such as soil moisture storage and evapotranspiration deficits—are extracted from the model simulations. Both the direct runoff outputs from the process‑driven models and these selected process variables are fed as additional input features into a Long Short‑Term Memory (LSTM) network, which is well suited for learning long‑term temporal dependencies in sequential data. This yields two hybrid variants that couple physically based simulations with data‑driven corrections. The performance of the hybrid models is comprehensively compared against that of the standalone process‑driven and data‑driven benchmarks, using multiple error and correlation metrics. [Results] The results demonstrate that the hybrid approach consistently outperforms all single‑model alternatives across almost evaluation metrics and forecast horizons. Specifically, the correlation coefficient between forecasted and observed runoff increases by 0.1 to 0.4 across four reservoirs and various lead times, indicating a notably improved agreement with observations. The mean absolute percentage error drops substantially, with reductions ranging from 10% to 40%, reflecting enhanced predictive stability. Moreover, the cumulative runoff volume errors accumulated over 3‑, 5‑, 10‑, and 15‑day windows are reduced by up to 15%, eventually falling within a range of 20% to 35%. In addition, the hybrid model effectively curtails both the frequency and magnitude of overestimated streamflow peaks, confining the upper envelope of forecasted discharges to a physically reasonable interval, which is particularly beneficial for reservoir operation and flood control. Beyond general flow simulation, the hybrid model also demonstrates marked superiority in flood event forecasting. It achieves a substantially higher flood peak hit rate, with improvements of up to 45% relative to the LSTM model, and reduces the relative error in peak flow magnitude by 20% across most events, highlighting the model's capability in capturing extreme hydrological responses. Most notably, the hybrid model corrects the extreme overestimation of peak flows at the Tuokou Reservoir—a persistent issue in the LSTM model, where the peak error once reached 95%—and brings this error down to within 10%, thereby offering a much more reliable warning signal for extreme flood events. Collectively, these results confirm that the hybrid architecture not only raises overall forecast accuracy but also enhances the model's robustness in capturing high-flow extremes, which is critical for real-time reservoir management and flood mitigation. [Conclusions] Overall, these findings indicate that enriching a deep learning model with both process‑based runoff simulations and key intermediate variables can effectively mitigate the deficiencies of either modelling paradigm alone, leading to more accurate and reliable short‑ to medium‑term forecasts for cascade reservoir systems. The proposed framework is computationally feasible and holds considerable promise for operational implementation in real‑time reservoir scheduling and early warning systems, especially in regions where high‑quality observed data are available for training and bias correction.