Drought is one of the most frequent and widespread natural disasters and has a tremendous impact on agriculture, ecology, society, and economy. Because the causes of drought are complex and there are many influencing factors, the applicability of drought indices has obvious regional and time-scale constraints. Among many drought indices, SPI (standardized precipitation index) using monthly precipitation data is simple to calculate and has multi-scale characteristics. And SPI is an indicator of the probability of precipitation occurring in a certain period. Moreover, SPI uses the probability density function to solve the cumulative probability and further normalizes the cumulative probability, eliminating the temporal and spatial distribution of precipitation. Therefore, the selected drought index is suitable for drought monitoring and evaluation of the climate above the monthly scale. In addition, the precipitation data are easily accessible and widely used in drought monitoring research. The drought index generally needs the observation data of the ground meteorological site for calculation. However, in practical applications, due to the spatial and temporal differences in the distribution of precipitation, limited ground station observations are difficult to truly reflect the spatial heterogeneity of precipitation, especially in areas where observation sites are sparse. Under these circumstances, the drought index calculated based on the data from meteorological site produced an error. With the rapid development of remote sensing technology, a series of precipitation products based on satellite remote sensing have emerged. Satellite-derived precipitation monitoring at high spatial resolution is essential for assessing the water and energy cycles at the global and regional scale. The Tropical Rainfall Measurement Mission (TRMM) satellite was successfully launched on November 27, 1997,and was the first meteorological satellite to measure tropical and subtropical precipitation. The TRMM 3B43 dataset used in this study was synthesized from the TRMM satellite data and other data. The spatial resolution of the TRMM 3B43 products is 0.25°*0.25° and extends from the latitude 50° S to 50° N. In order to evaluate the applicability of high spatial-temporal resolution satellite remote sensing precipitation products in drought monitoring, based on the tropical rainfall satellite products and station-based meteorological data, the SPI values at different time scales (1,3,6,and 12 months), which only used precipitation and could monitor both dry and wet conditions, were calculated during the period of 1998-2016 in Henan Province. The results showed that the monthly precipitation data of TRMM 3B43 have the high correlation with the observed data of meteorological stations, and the correlation coefficient is higher than 0.9. Except a few cases, TRMM 3B43 slightly overestimates the precipitation. In addition, there is a good agreement between the different time scales (1,3,6 and 12 months) calculated by the 2 data sources, and the fluctuation amplitude decreases with the increase of timescale. In addition, droughts occurred in the years of 1999,2001,2012 and 2013. Moreover, the correlative analysis was performed based on the SPI values at different time scales from the 2 data sources. The correlation coefficient was higher than 0.7. The consistency of the SPI values calculated by the 2 data sources at different time scales was high, indicating that TRMM data can replace site observation data for drought monitoring and evaluation. Therefore, our study on the suitability of the TRMM satellite precipitation data in regional drought monitoring can provide a new way for effective monitoring of meteorological and agricultural drought.