The daily rainfall is the most important and demanded input of water resources studies, challenged by typically low density and/or poor quality of in-situ observations. However, the satellite earth observation, through freely available web-based products, can provide complementary rainfall data. Such data is however, typically affected by substantial error, particularly at daily temporal resolution. Therefore, effective methods and protocols of rainfall downscaling, validation, and bias-correction are needed. The aims of this study were to: i) validate two downscaled satellite-derived daily rainfall products, CHIRPS and MPEG, against in-situ observations; ii) merge the downscaled products with in-situ observations to improve their accuracy and evaluate them to select better performing one. This study was conducted at topographically complex, Upper Tekeze Basin (UTB), separately for the wet and dry seasons, within 1 January 2015 – 31 December 2018. Validation of the products, downscaled by nearest-neighbor (NN) and bilinear (BL) methods, was carried out using descriptive statistics, categorical statistics and bias decomposition methods, introducing novel protocol with new bias indicators for each of the evaluation methods. The validation showed large biases of CHIRPS and of MPEG, larger for CHIRPS than for MPEG, larger in dry than in wet season and slightly larger for NN than for BL. To correct biases of the downscaled CHIRPS and MPEG, each was merged with the in-situ observed rainfall applying Geographically Weighted Regression (GWR) algorithm and using rainfall dependence on altitude as explanatory variable. The GWRmerging method substantially improved the accuracy of the MPEG and CHIRPS, with slightly better final accuracy of MPEG than of CHIRPS, better in wet than in dry season. This study confirmed that GWR-merged method could substantially reduce daily bias of satellite rainfall products, even in topographically complex areas, such as the UTB. Further improvement of the method application, can be achieved by densifying raingauge network and eventually by adding accuracy-effective explanatory variable(s).