Precipitation provides the most crucial input for hydrological modeling. Rainfall Estimation from rain gauges is the most common and traditional method have been used widely to measure rainfall at catchment scales. In many developing countries, a dense rain-gauge grid is generally unavailable, suffering from a sparse station distribution at high altitudes or in rural areas. Recent advances in remote sensing technologies have provided precipitation data with high spatial and temporal resolution. Accurate information on the benefits and deficits of these datasets is often lacking, especially over Iran. This study aims to provides a comprehensive evaluation of a good variety of state-of-the art precipitation datasets against 41 synoptic gauge observations, as a reference in the period of 2013 to 2020 over Iran. In particular, the performance of ERA5 as reanalysis, PERSIANN as satellite based, CHIRPS and PERSIANN-CDR as satellite-gauge precipitation products at daily, monthly and annual scale has been assessed. Statistical metrics, precipitation detection capability and false alarm ratio have been used to measure the accuracy of each product over spatial and time scales. The result show that over annual and daily scale PERSIANN-CDR product outperforms, and over daily scale PERSIANN-CDR and CHIRPS products perform well compared to ERA5 and PERSIANN products. The CHIRPS and PERSIANN-CDR products deliver reliable and useful ability of precipitation detection comparing to other products.