In this study, we use the global medium-term ensemble forecast precipitation data from three weather forecast centers, CMA, NCEP and ECMWF, in the TIGGE database, all with a spatial and temporal resolution of 0.5°/6h, ensemble memberships of 30, 30, and 50, and foresight periods of 360h, 360h, and 384h, in that order.

  The forecast operation times of CMA and ECMWF are at Universal Time (UTC) of 00 and 12 o'clock, and NCEP at 00, 06, 12 and 18 o'clock, and the numerical model of all three is a combined perturbation scheme of stochastic physical tendency and stochastic kinetic energy compensation. The data can be downloaded at https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/.

  Mainly using python tools, the steps are:

 (1) Aggregate all the ensemble members of CMA, NCEP, and ECMWF together by equal weight integration to form a multi-model super-ensemble forecast data CNE;

 (2) Adopt the bilinear interpolation method to refine the 0.5° spatial resolution of CNE to 0.1°;

 (3) Construct an ensemble forecast statistical post-processing model based on U-net deep neural network for the downscaled CNE;

 Development of statistical post-processing datasets for data forecasting in the lower Yangtze River and Lower Rivers for 2021-2022 based on the constructed model.

  After the U-net revision, the categorical indicator False Alarm Rate (FAR) is reduced from about 0.70 to about 0.55, and the Equitable Threat Score (ETS) is improved from about 0.15 to about 0.3, and the average gain of the foresight period reaches 0.17 and 0.14, respectively. the quantitative indicator KGE' improves under most of the foresight periods, and the effective maintains the spatial structure of the data. The comprehensive accuracy index CAS is effectively improved, and the foresight period is effectively extended up to 54 h. Therefore, the present data can provide a high-quality forecast precipitation dataset for the lower reaches of the Yangtze River and the Lower Lower River region.