Accurate and timely information about soil moisture (SM) is not only critical to the study of climate change, but is also significant for agricultural production, drought monitoring, and hydrometeorological predictions. With the development of satellite remote sensing techniques, land surface models, and data assimilation techniques, a number of global seamless soil moisture products have been released with highly variable accuracy. A comprehensive evaluation of these products is necessary to select a suitable SM product for use. In this study, we provided such an evaluation of four global seamless SM products (SMAP-L4, ERA5-Land, GLDAS-Noah, and GLEAM) over China based on the stations from the automatic soil moisture observation (ASMO) system. Compared to previous studies that focused on a direct comparison between SM products and in situ SM observations, we specifically evaluated these products from the perspective of SM sensitivity to precipitation. This is because in situ precipitation observations are more spatially representative than in situ SM observations. Precipitation is a regional event, therefore in situ precipitation observations can be better scale-matched with coarse resolution SM products compared to in situ SM observations. Further, in addition to surface soil moisture (SSM), root zone soil moisture (RZSM) was evaluated in this study. The combination of all statistics illustrates that SMAP-L4 and ERA5-Land generally performed better than GLDAS-Noah and GLEAM. Regardless of the SSM or RZSM estimates, SMAP-L4 had the lowest median bias and RMSE of the four products, and ERA5-Land had the lowest median correlation coefficient (R) value. The lowest median ubRMSE was obtained by SMAP-L4 in SSM evaluation and ERA5-Land performed better than other RZSM products with the lowest median ubRMSE. From the perspective of SM sensitivity to precipitation, SMAP-L4 can capture daily precipitation dynamics most effectively, closely followed by ERA5-Land. This partially explains the superiority of SMAP-L4 and ERA5-Land over GLDAS-Noah and GLEAM for SM monitoring. The results of the metrics under different precipitation conditions varied considerably among the products. But while considering of ubRMSE alone, all products performed better under arid and humid conditions than to semi-arid and semi-humid conditions. This may be due to the fact that SM products had a limited ability to capture the SM dynamics under moderate precipitation. These findings may provide new insights into the selection of appropriate SM products and SM estimation in the future.