In this paper, we discuss the problem of efficiently handling temporal queries on Hyperledger Fabric, a popular implementation of Blockchain technology. The temporal nature of the data inserted by the Hyperledger Fabric transactions can be leveraged to support various use-cases. This requires that the temporal queries be processed efficiently on this data. Currently this presents significant challenges as this data is organized on file-system, is exposed to users via a limited API and does not support any temporal indexes. We present two models for overcoming these limitations and improving the performance of temporal queries on Fabric. The first model creates a copy of each event inserted by a Fabric transaction and stores temporally close events together on Fabric. The second model keeps the event count intact but tags some metadata to each event being inserted on Fabric s.t. temporally close events share the same metadata. We discuss these two models in detail and show that these two models significantly outperform the naive ways of handling temporal queries on Fabric. We also discuss the performance trade-offs for these two models across various dimensions - data storage, query performance, data ingestion time etc.