<p>A well-designed network of rain gauges is essential for effective water resources management. The optimal design can be achieved by employing various objective functions, such as variance and entropy, which have been explored in numerous studies. In contrast to the existing literature, this paper introduces a new method for designing rain gauge networks that incorporates global sensitivity analysis (GSA). Instead of using objective functions, we aim to address issues such as the impact of uncertain input factors on model output uncertainty. Accordingly, this study employs variance decomposition as an important tool in GSA arena coupled with block ordinary kriging (BOK) model to decompose the uncertainty of the model output into components attributed to the uncertainty in each input variable. In addition, to streamline our rain gauge network design, principal component analysis (PCA), was employed to ascertain the redundancy inherent within the existing rainfall network and to find the most efficient number of gauges. Subsequently, cluster analysis was employed to select the specific stations, based on the maximum value of variance decomposition within each cluster. To assess the efficacy of the suggested methodology, this coupling technique is implemented to a real scenario in Southwestern Iran. The proposed approach demonstrates comparable performance to the current methods in design of rain gauge network for desired information level. Consequently, the proposed approach can be used in network design particularly, when the curse of dimensionality is an issue and the cost of the computational demands become prohibitively high for intermediate values of the data set.</p>