In the era of big data and cloud computing， querying and analyzing temporal big data faces many important challenges. Focused on the issues such as poor query performance and ineffective utilization of indexes for temporal aggregation range query， a Distributed Temporal Index （DTI） for temporal aggregation range query was proposed. Firstly， random or round-robin strategy was used to partition the temporal data. Secondly， intra-partition index construction algorithm based on timestamp’s bit array prefix was used to build intra-partition index， and partition statistics including time span were recorded. Thirdly， the data partitions whose time span overlapped with the query time interval were selected by predicate pushdown operation， and were pre-aggregated by index scan. Finally， all pre-aggregated values obtained from each partition were merged and aggregated by time. The experimental results show that the execution time of intra-partition index construction algorithm of the index for processing data with density of 2 400 entries per unit of time is similar to the execution time for processing data with density of 0.001 entries per unit of time. Compared to ParTime， the temporal aggregation range query algorithm with index takes at least 22% less time for each step when querying the data in the first 75% of timeline and at least 11% less time for each step when executing selective aggregation. Therefore， the algorithm with index is faster in most temporal aggregate range query tasks and its intra-partition index construction algorithm is capable to solve data sparsity problem with high efficiency.