Increasing applications of spatial interpolation methods SIM specialty particularity to generating surface continuous data in climatology science so much missing data, and as with any other statistical techniques, it also produces a degree of errors associated with the estimation, so there is also a big interest in their accuracy. Various SIMs were compared in the geographical information system environment to assess it is performance and compare the effectiveness of analysis for assessing the spatial distribution of the Relative Humidity dataset (2010-2022) in the Western Desert of Egypt. Three SIMs were used to generate the spatial distribution of the studied indicator: inverse distance weighting, Completely Regularized Spline and Ordinary Kriging. Cross-validation was used to evaluate the SIM's accuracy by comparing the values of mean error, mean Standardized error and root mean square error. Based on the results there tend to be more changes from the north to the south of the Study area. It is affected by closer water areas. There is a significant difference in the implementation of three SIMs, but if we look at the prediction error Values, it can be said that the OK is better than the IDW and CR_Spline SIM special in low-density data RMSE: 0.8, MSE: 4.1, ME: 0.1. But in the used high data density the CR_Spline is the best performance with interpolated the RH in with ME 0.03 and MES 1.9 With this research, it is hoped that it can become a consideration of the effect of the Network Density on the performance of the spatial interpolation techniques.