This study aims to investigate the effect of the topography on the estimation of unsampled meteorological/climate data which are widely used for modeling climate and renewable energy potentials. In this context, firstly, one of the multi parameter geostatistical interpolation methods; co-kriging was applied on a specific data set including precipitation, wind speed and temperature values measured at 36 meteorological monitoring stations located in Sakarya River Basin, Turkey. Cokriging results were then compared with single parameter deterministic (IDW, NN) and geostatistical interpolation methods (Kriging). Secondly, multivariate linear regression (MLR) model was used to understand the correlation between climate parameters and topography. Accuracies of the applied methods were assessed by using cross validation technique that allows comparison of estimated and measured values by using only the information available in sample data set. Spatial interpolation results of the study mainly outlined that the use of Cokriging lowered the estimation error for wind speed and temperature. Thus, topography (latitude, longitude, elevation) was proved to be more influential on wind speed data prediction than precipitation and temperature prediction. On the other hand, MLR results demonstrated that topographic parameters had a significant effect on only temperature prediction for this specific application.