The interpolation prediction of spatial data usually uses traditional spatial interpolation methods such as inverse distance weighted interpolation and Kriging interpolation, whose prediction accuracy is relatively low under the impact of marginal distribution or outlier, as a result, the method based on copula overcomes the problem. Spatial correlation structures are described by PairCopula function and the parameters are estimated, and spatial interpolation prediction method is discussed in corresponding values of noneobservation stations based on spatial data. This model is compared with inverse distance weighted interpolation, original Kriging interpolation and universal Kriging interpolation based on the data of fog in Chongqing, and the results show that the spatial prediction model based on PairCopula function posses the higher accuracy.