This study elucidates the spatiotemporal characteristic of June—July mean Meiyu rainfall over the middle and lower reaches of the Yangtze River basin(27°—33°N,108°—123°E) using Chinese monthly gauge precipitation data and global atmospheric reanalysis datasets from 1961 to 2000.Three physically meaningful precursors play pivotal roles in enhancing Meiyu rainfall during June and July.First,positive sea level pressure anomalies over the subtropical western Pacific (SWP) during April—May strengthened the western North Pacific subtropical high by exciting Kelvin wave responses and enhancing Walker circulation.This phenomenon facilitates moisture transport from the tropics to the Yangtze River via southerly winds.The mechanism underlying SWP’s impact on Meiyu highlights the persistent influence of atmosphere-ocean interaction over the Indo-Pacific basin from spring to summer.Second,the negative tendency of sea level pressure over the North Atlantic from March to May (NAP) reflects the influence of North Atlantic Oscillation (NAO)-related mid-latitude wave trains on Meiyu.From spring to early summer,the evolution of NAO-related wave trains across Eurasia strengthens the Northeast Asian cyclone and enhances Meiyu rainfall.Third,the cooling tendency of surface temperature over East Siberian from January to April (EST) is closely associated with the extratropical westerly jet by amplifying the temperature gradient between the tropics and polar regions.This condition favors the maintenance of meridional circulation over East Asia and enhances Meiyu rainfall.The aforementioned mechanisms have been verified in corresponding numerical experiments based on a linear baroclinic model.Consequently,a physically-based empirical (PE) model based on these three predictors exhibited significant prediction skills,with a temporal correlation coefficient (TCC) of 0.79 and 0.77 and a mean square skill score (RMSE) of 0.59 and 0.68 during the training period (1961—2000) and independent forecast period (2001—2022),respectively.For comparison,the partial least squares (PLS) regression method and five machine learning methods (Random Forest,LightGBM,Adaboost,Catboost,and XGboost) are employed to conduct seasonal predication of Meiyu based on the same potential precursors.Although the PLS model and five machine learning models exhibit prefect hindcast skills (TCCs of LightGBM,Catboost,and XGboost all being 1.00) during the training period,their skills diminish dramatically in the independent forecast period of 2001—2022 (with the maximum TCC being 0.43 and the minimum RMSE being 0.94),indicating a significant overfitting problem.Hence,the PE model based on physically meaningful precursors demonstrates superior and stable independent prediction skills in Meiyu rainfall forecasts.The findings of this study underscore the advantages of the PE model and emphasize caution in the use of machine learning methods in climate prediction.Additionally,the comparison of multiple methods for seasonal prediction of Meiyu in this study provides practical scientific references for operational departments engaged in seasonal climate prediction.