Abstract: At present, precipitation products still have great uncertainty. Precipitation and its spatial distribution can be estimated more accurately by using multi-source precipitation data fusion. To achieve data fusion in no-gauged areas, Bayesian-Three Cornered Hat method is adopted to integrate precipitation products based on gauged data, satellite remote sensing and reanalysis data without any prior information, to explore the influence of precipitation products with different input quantities on the accuracy of fusion data, and to study the contribution rates of each precipitation product to the accuracy of fusion data. It is applied in the source region of the Yellow River. The results show that the performance of the fusion data is better than that of the original precipitation products on the monthly scale. On the daily scale, the performance of the fusion data is obviously better than that of satellite remote sensing and reanalysis precipitation products, but lower than that of the gauge-based precipitation product CHM_PRE. Two gauge-based precipitation products, CN05. 1 and CHM_PRE, have the largest contribution rates to the fusion data. The application in the source region of the Yellow River shows that the Bayesian-Three Cornered Hat method can estimate precipitation more accurately. It is suitable for no-gauged areas, and can provide the reference basis for data fusion analysis and its application.