Abstract: Changes in precipitation have great impacts on regional terrestrial ecosystems and water cycles. In this study, the middle and lower reaches of the Yangtze River Basin is considered to be the study area. The satellite-ground collaborative precipitation derived from in situ meteorological station, TRMM and GPM from 2001 to 2019 were collected. The interpolation results of satellite-ground collaborative precipitation against six Anusplin interpolation models were compared with the TRMM and GPM precipitation, TRMM and GPM downscaling precipitation, and interpolation precipitation based on the measured precipitation of verification stations. The research result can provide theoretical support for obtaining the precipitation with high accuracy, high resolution and excellent spatial details in the areas with sparse meteorological station. The results show that:（1）both the multi-year average accuracy of the results of the TRMM （R²=0.81, BIAS=0.06, RMSE=171.1mm）and GPM satellite-ground collaborative interpolation models（R²=0.81, BIAS=0.07, RMSE=172.8mm） in the middle and lower reaches of the Yangtze River Basin from 2001 to 2019 were superior to the multi-year average accuracy of interpolation precipitation of in situ meteorological stations（R²=0.66, BIAS=0.02, RMSE=198.66mm）, TRMM downscaling precipitation（R²=0.79, BIAS=0.06, RMSE=174.8mm）, and GPM downscaling precipitation（R²=0.81, BIAS=0.09, RMSE=192.4mm）.（2）The satellite-ground collaborative interpolation precipitation has obvious advantages in the spatial detail expression, image integrity, and model stability. The interpolation result of TRMM satellite-ground collaborative interpolation model 5 has the best accuracy.（3）The variable and spline number of Anusplin interpolation model have a stronger impact on the accuracy of the interpolation result based on in situ meteorological station, but a weaker impact on the interpolation result based on satellite-ground collaborative interpolation precipitation.（4）The result of the downscaling model is closely related to auxiliary variables, which may cause a certain loss of accuracy and image deformity to downscaling precipitation data.

Key words: Satellite-ground collaborative, TRMM, GPM, Downscaling, Anusplin interpolation model