The interconnection of geographically distributed supercomputers via highspeed networks makes available the needed compute power for large-scale scientific applications, such as finite element applications. In this paper we propose a two-level data decomposition method for efficient execution of finite element applications on a network of supercomputers. Our method exploits the following features that may be different for each supercomputer in the system: processor speed, number of processors used from each supercomputer, local network performance, wide area network performance and wide area topology. Preliminary experiments involving a nonlinear, finite element application executed on a network of two supercomputers, one located at Argonne National Laboratory and the other one at the Cornell Theory Center, demonstrate a 20% reduction in execution time when the proposed decomposition is used as compared with naively applying conventional decompositions that are applicable to single supercomputers.