The performance of the Version 06B monthly Integrated Multi-satellite Retrievals for GPM (IMERG) as well as three satellite precipitation products, including the Version 7 monthly TRMM Multi-satellite Precipitation Analysis (TMPA 3B43), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is evaluated on multiple temporal scales over a typical agriculture area in China (Huanghuaihai Plain) from 2015 to 2017. The results show that four satellite-based precipitation products have good agreement with rain gauge measurements at annual scale (mean CC: 0.92, mean RMSE: 205.52 mm, mean RB: 6.57%) and on a seasonal scale (mean CC: 0.88, mean RMSE: 67.04 mm, mean RB: 9.57%) but perform relatively poorly on a monthly scale with mean CC of 0.83. All satellite precipitation products can capture the spatial distribution pattern of rain gauge that performs adequately in spring and winter both with a mean CC of 0.95. However, except for IMERG V06B (CC: 0.90, RMSE: 86.99 mm, RB: 6.62%), other satellite precipitation datasets showed poor performance in summer with a mean CC of 0.76 and a mean RMSE of 133.88 mm. Overall, the IMERG V06B product has the best performance on the monthly, seasonal and annual scales over the Huanghuaihai Plain. The reason is that IMERG V06B, with higher spatiotemporal resolution and improvement of retrieval algorithm, can obtain more detailed precipitation information. In addition, GPM carries the first spaceborne Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a multi-channel GPM Microwave Imager (GMI), which makes it easier to detecting light rainfall and snowfall. The results also show that the performance of IMERG V06B at each individual gauge station is better than those of other products with lower RMSEs and higher CCs. Moreover, the relatively high probability of detection (POD) and low false alarm ratio (FAR) of the IMERG V06B product over most ranges of precipitation thresholds also indicated the good light rainfall and snowfall detection capability over the Huanghuaihai Plain. However, the performance of satellite-based precipitation products is affected by local climate and topography, which lead to poor performances in summer and early autumn due to heavy rainfall over the Huanghuaihai Plain. Therefore, the estimation accuracy of satellite-based precipitation products in different regions needs to be improved in the future. This study will contribute to the improvement of satellite retrieval algorithm and provide guidance in agricultural production and natural hazard prevention in the Huanghuaihai Plain.