Abstract: In meteorological models, land surface processes are crucial for simulating accurate numerical patterns. The land surface can be determined by physical properties of soil and soil state variables. The purpose of the present study is to implement a new soil map generated by Beijing Normal University (BNU) in China and revised hydrologic soil parameters different to defaults in the Weather Research and Forecasting model (WRF), and value their influences on the forecast skill over North China during the warm season using the Noah land surface model. A three months (from 1 June to 31 August 2017) simulation by the WRF model shows that the BNU soil map and the revised hydrologic soil parameters can obviously improve the simulation of ground meteorological elements. Loamy soil is the dominant soil type over eastern China based on the BNU soil dataset, whereas clay loam is the dominant one in WRF default soil dataset. Soil water content at field capacity is greater in the revised soil parameters dataset than that in the WRF default, which results in enhanced direct evaporation from the top shallow soil layer. Colder 2 m temperature and wetter 2 m humidity are found in the simulation with updated soil parameters because of reduced heat flux and enhanced latent heat flux from surface to the atmosphere. Evaluation against observations at 748 surface meteorological stations shows that the root mean square errors are reduced by 3.4% and 2.9% for 2 m temperature and 2 m humidity respectively with updated soil texture and soil parameters.