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Many distributed applications rely on the strong guarantees of sequential consistency to ensure program correctness. Replication systems or frameworks that support such applications typically implement sequential consistency by em- ploying voting schemes among replicas. However, such schemes suffer dramatic performance loss when deployed globally due to increased long-haul message latency between replicas in separate data centers. One approach to overcome this challenge involves deploying distinct instances of a service in each geographic cluster, then loosely coupling those services. Unfortunately, the consistency guarantees of the individual replication system in- stances do not compose when coupled this way, sacrificing overall sequential consistency. We propose an alternative approach, the consistent, propagatable partition tree (CoPPar Tree), a data structure that spans multiple data centers and data partitions, and that realizes sequential consistency using divide-and-conquer. By leveraging the geospatial affinity of data used in global services, CoPPar Tree can localize reads and writes in a sequentially consistent manner, improving the overall performance of a sequentially consistent service deployed at global scale. Our work allows clients to access local data and fully run SMR protocols locally without additional overhead. We implemented CoPPar Tree by enhancing ZooKeeper with an extension called ZooTree, which can be deployed without changing existing ZooKeeper clusters, and which achieves a speedup of 100×for reads and up to 10× for writes over prior work.