Scientific I/O libraries, like PnetCDF, ADIOS, and HDF5, have been commonly used to facilitate the array-based scientific dataset processing. The underlying physical data layout information, however, is usually hidden from the upper layer's logical access. Such mismatching can lead to poor I/O. In this research, we have observed performance degradation in the case of concurrent sub-array accesses, where overlaps among calls that access sub-arrays led to high contention on storage servers due to the logical-physical mismatching. We propose a locality-driven high-level I/O aggregation approach to address these issues in this work. By designing a logical-physical mapping scheme, we try to utilize the scientific dataset's structured formats and the file systems' data distribution to resolve the mismatching issue. Therefore the I/O can be carried out in a locality-driven fashion. The proposed approach is effective and complements the existing I/O strategies, such as the independent I/O and collective I/O strategy. We have also carried out experimental tests and the results confirm the performance improvement compared to existing I/O strategies. The proposed locality-driven highlevel I/O aggregation approach holds a promise for efficiently processing scientific datasets, which is critical for the data intensive or big data computing era.