Abstract: This study first uses the volumetric soil water content data from 732 stations in China, as provided by the National Meteorological Information Center (NMIC) at the China Meteorological Administration (CMA), to evaluate the simulations of the Community Land Model version 4.5 (CLM4.5) driven by Climate Forecast System Reanalysis (CFSR) near surface meteorological data (referred as to CLM4.5-CFSR). Then the CLM4.5-CFSR is used to investigate the spatial-temporal characteristics of soil moisture memory in China region during 1980–2009. The soil moisture memory is calculated by both Pearson correlation method and autocorrelation method. The effects of precipitation frequency, precipitation intensity and near-surface temperature on soil moisture memory are then explored. The results show that CLM4.5-CFSR can reflect the soil moisture changes on the monthly time scale in China. The spatial distributions of soil moisture memory from two methods are similar, but their seasonal characteristics are different. The soil moisture memory duration does not vary very much with soil depth, ranging from 0.85–2.2 months across China with relatively larger magnitude in the northeast of the Inner Mongolia, while smaller in the southwest of Xinjiang Province. In spring, the wetter the soil, the longer the soil moisture memory. When the precipitation frequency is low, it has little effect on soil moisture memory in areas where the evaporation rate is high. When the precipitation intensity is high, soil moisture memory will be shortened by it because it can replenish the soil water rapidly and destroy the initially dry or wet conditions of the soil. Changes in the near-surface temperature will shorten the soil moisture memory through its effect on the soil evaporation. In the future, it is necessary to carry out climate model sensitivity experiments to gain insights on physical mechanisms associated with the conclusions obtained in the current study, which in turn to provide a basis for further improvements of the seasonal and intraseasonal precipitation prediction.