In this study, the Spatio-temporal performance of 5 global gridded precipitation datasets including GPCC V8, CHIRPS V2, ECMWF ERA5, NASA MERRA2, and PERSIANN-CDR (PCDR) in drought monitoring has been evaluated. For this purpose, the standardized precipitation index (SPI) and precipitation information of 13 synoptic stations of the Meteorological Organization of Iran during the thirty years of 1987-2016 has been used. Comparisons were carried out based on performance indices include correlation, mean square root error (RMSE), Nash-Sutcliffe efficiency coefficient, and modified agreement index (MAI) as well as drought detection accuracy metrics including False Alarm Ratio (FAR), probability of detection (POD) and the Critical Success Index (CSI). The results showed that GPCC, ERA5, PCDR datasets had a strong agreement with SPI observations so that they showed the drought trends and situations well and their R2 with observational SPI was <0. 90, <0. 89, and <0. 90, respectively. Also, their RMSE was lower than CHIRPS and MERRA2 and their Nash Sutcliffe and MAI coefficients were higher and in most parts of the watershed, especially in the northeast and southwest, GPCC, and then ERA5 and PCDR have a high correlation and NSE. The results also revealed that GPCC, ERA5, and PCDR datasets have considerable potential in detecting drought events, especially in SPI <-1. However, in severe drought events, the CSI of all datasets has shown a declining trend and thus the ability to detect drought events has reduced. Furthermore, CHIRPS and MERRA2 have shown moderate and poor performance in drought monitoring of this watershed. Eventually the results of this study, in turn, can provide the knowledge needed to improve drought monitoring systems, which will be very effective and useful in drought risk management and adaptation planning to reduce drought damage.