Abstract: High-dimensional data interpolation is a basic content of big data analysis.The computa-tional cost of traditional Kriging is of order n3,which means computational complexity grows as thethird power rate along with the increase number of the observations.So Kriging computing time canhardly meet real-time applications.Thus,Fixed Rank Kriging(FRK)rises in response.To achievehigh computing speed,it simplifies computational complexity by reducing the dimensions of large-di-mensional matrix.Under the background of big data analysis,this paper uses FRK to do the statisti-cal modeling and implement interpolation analysis with the global MODIS temperature.It also givesout Root-Mean-Square standardized which equals to 1.0003through the cross-validation method,indi-cating that FRK can provide high accurate interpolation result.On the side of computational efficien-cy,with increasing data,the computational cost of FRK increases as a slow and steady speed while or-dinary kriging tends to an exponential growth.The conclusion can be drawn from two points above,that is,FRK can reduce computational complexity and shorten interpolation time with high predictionaccuracy when compared with ordinary kriging.