Rainfall is considered a highly valuable climatologic resource, particularly in arid regions. As one of the primary
inputs that drive watershed dynamics, rainfall has been shown to be crucial for accurate distributed hydrologic
modeling. Precipitation is known only at certain locations; interpolation procedures are needed to predict this variable
in other regions. In this study, the ordinary cokriging (OCK) and collocated cokriging (CCK) methods of
interpolation were applied for rainfall depths as the primary variate associated with elevation and surface elevation
values as the secondary variate. The different techniques were applied to monthly and annual precipitation data
measured at 37 meteorological stations in the Central Kavir basin. These sequential steps were repeated for the mean
monthly rainfall of all twelve months and annual data to generate rainfall prediction maps over the study region. After
carrying out cross-validation, the smallest prediction errors were obtained for the two multivariate geostatistical
algorithms. The cross-validation error statistics of OCK and CCK presented in terms of root mean square error
(RMSE) and average error (AE) were within the acceptable limits for most months. Then the two approaches were
compared to select of the most accurate method (AE close to zero and RMSE from 0.53 to 1.46 for 37 rain gauge
locations for all months). The exploratory data analysis, variogram model fitting, and generation precipitation
prediction map were accomplished through use of ArcGIS software.