As a component in seamless weather-climate prediction, subseasonal forecast provides essential guidance for decision-making across various sectors. Utilizing hindcasts from six models in subseasonal-to-seasonal prediction project database, we investigated deterministic and probabilistic multi-model 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 given ensemble size, a selective ensemble composed of the best-performing models achieves comparable or even slightly better performance than a balanced full-model ensemble. This highlightes the importance of strategic model weighting in MME construction. Two weighted MME models—calibrated using censored and shifted gamma (CSG) and generalized extreme value (GEV) distribution-based ensemble model output statistics—exhibit significantly enhanced skill compared to the equal-weighted MME, particularly in the first week. To address forecast skill degradation in week2, we finally proposed a novel conditional MME approach, and developed ENSO-conditioned weighted MME models (c-CSG and c-GEV) through incorporating the preceding winter ENSO phase. A 3-year independent forecast test and a case study of a heavy rainfall event demonstrate that the ENSO-conditioned weighted MME models outperform the conventional MME approach, highlighting their potential to enhance subseasonal precipitation forecast capabilities.
 

subseasonal forecast,multi-model ensemble,heavy rainfall event,ENSO,conditional multi-model ensemble