Satellite constellation network is a powerful tool to provide ground traffic businessservices for its continuous global coverage. For the resource-limited satellite network, it isnecessary to predict satellite coverage traffic volume (SCTV) in advance so as to properly allocateonboard resources for better task fulfillment. Traditionally, a global SCTV distribution data table isfirst statistically constructed on the ground according to the historical data and uploaded to the satellite. Then SCTV is predicted onboard by the data table lookup. However, the cost of the large data transmission and storage is expensive and prohibitive for satellite. To solve these problems, this paper proposes to distillate the data into surrogate model to be uploaded to satellite, which can both save the valuable communication link resource and improve the SCTV prediction accuracy compared to table lookup. An effective surrogate ensemble modeling method is proposed in this paper for better prediction. First, according to the prior geographical knowledge of the SCTV distribution, the global earth surface domain is split into multiple sub-domains. Second, on each sub-domain, multiple candidate surrogates are built. To fully exploit these surrogates and combineinto a more accurate ensemble, a partial weighted aggregation method (PWTA) is developed. For each sub-domain, PWTA adaptively selects the candidate surrogates with higher accuracy as the contributing models, based on which the ultimate ensemble is constructed for each sub-domainSCTV prediction. The proposed method is demonstrated and testified with an air traffic SCTV engineering problem. The results demonstrate the effectiveness of PWTA regarding good local andglobal prediction accuracy and modeling robustness.