Several factors influence the interpolation accuracy of point source rainfall data during storms. Types of storm, network density and interpolation method are the most significant factors. Usually, random distributed point source data is converted into regular distributed grids, through the appropriate method. Kriging is a stochastic method that simulates the spatial surface using sample points, based on best-fit Semi-Variogram (SV) models. This study assesses the effect of pixel size on the performance of Kriging interpolation using the Gaussian SV model. Twenty eight rainfall stations located at the upper part of the Klang River Basin (KRB), Malaysia are selected and three storm events are investigated. Simple Kriging interpolation is applied using different pixel size ranges from 50 to 3000 m. This study shows that pixel size is important issue when explaining the spatial pattern of rainfall. Optimal pixel size depends on the variance, minimum distance of pair points or rainfall network density and the visualization requirement. It is difficult to maintain a certain pixel size but based on the drawn result, it can be concluded that a pixel size at the range of 200 to 500 m is more appropriate for this region.