Range query is a very important operation to support On-Line Analytical Processing (OLAP) in data warehouses. Although several cube storage structures for range sum queries and dynamic updates have been introduced recently. However, the complexities of both space and time are too higher to realistic. To solve this problem, a hierarchical data cube (HDC) and corresponding algorithms are provided in this paper. Both of the range query and update costs of HDC are O(log^dn), and the overall cost is O((logn)^2d) (under the C_qC_u model) or O(K(logn)^d) (under the C_qn_q+C_un_u model). The analytical and experimental results show that the costs of HDCs range queries, dynamic updates, storage space and the overall performance of HDC are superior to other cubage storage structures.