Abstract As a component in seamless weather-climate prediction, subseasonal forecast provides 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 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 fixed ensemble size, the balanced full-model ensemble always outperform 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 distribution (CSG) and the generalized extreme value distribution (GEV)—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 (c-CSG and c-GEV) through incorporating the observed preceding winter ENSO condition. The 3-year 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.
Hu et al. (Tue,) studied this question.