Improving Subseasonal Forecast of Summer Precipitation in the Middle–Lower Reaches of Yangtze River through Multimodel Ensemble

Abstract As a component in seamless weather–climate prediction, subseasonal forecasts provide essential guidance for decision-making across multiple sectors. Utilizing hindcasts from six models in Subseasonal to Seasonal Prediction project database, we investigated deterministic and probabilistic multimodel ensemble (MME) forecasts of summer precipitation and heavy rainfall events in the middle–lower reaches of Yangtze River at lead times of 1–4 weeks. Evaluations indicate that MME forecast skill improves as the ensemble size increases. For a fixed ensemble size, the balanced full-model ensemble always outperforms single-model ensemble, while remaining comparable or even slightly inferior to the selectively constructed ensemble comprising only the best-performing models. This highlights the importance of strategic model weighting in MME construction. Two weighted MMEs—respectively, calibrated using ensemble model output statistics based on the censored and shifted gamma (CSG) distribution and the generalized extreme value (GEV) distribution—exhibit significantly enhanced skill compared to the equal-weighted MME, particularly in the first week. To address forecast skill degradation in week 2, we finally proposed a novel conditional MME approach and developed ENSO-conditioned weighted MMEs [ENSO-conditioned CSG (c-CSG) and ENSO-conditioned GEV (c-GEV)] through incorporating the observed preceding winter ENSO condition. The 3-yr independent forecast test and case study of a heavy rainfall event demonstrate that the ENSO-conditioned MMEs outperform the conventional MMEs, highlighting their potential to enhance subseasonal precipitation forecast capabilities. Significance Statement Skillful subseasonal forecasts of monsoon rainfall offer critical support for decision-making in agriculture, water resource management, and disaster preparedness. This study investigates deterministic and probabilistic multimodel ensemble (MME) forecasts of summer rainfall and heavy rainfall events in the middle–lower reaches of the Yangtze River at lead times of 1–4 weeks. We highlight the importance of strategic model weighting in MME construction and propose a novel ENSO-conditioned weighted MME approach, demonstrating its potential to improve subseasonal precipitation forecast.