It is estimated that more than 500 eddy covariance sites are operated globally, providing unique information about carbon and energy exchanges between terrestrial ecosystems and the atmosphere. These sites are often organized in regional networks like CarboEurope-IP, which has evolved over the last 15 years without following a predefined network design. Data collected by these networks are used for a wide range of applications. In this context, the representativeness of the current network is an important aspect to consider in order to correctly interpret the results and to quantify uncertainty. This paper proposes a cluster-based tool for quantitative network design, which was developed in order to suggest the best network for a defined number of sites or to assess the representativeness of an existing network to address the scientific question of interest. The paper illustrates how the tool can be used to assess the performance of the current CarboEurope-IP network and to improve its design. The tool was tested and validated with modeled European GPP data as the target variable and by using an empirical upscaling method (Artificial Neural Network (ANN)) to assess the improvements in the ANN prediction with different design scenarios and for different scientific questions, ranging from a simple average GPP of Europe to spatial, temporal, and spatiotemporal variability. The results show how quantitative network design could improve the predictive capacity of the ANN. However, the analysis also reveals a fundamental shortcoming of optimized networks, namely their poor capacity to represent the spatial variability of the fluxes.