Scientific workflows typically communicate data between tasks using files. Currently, on public clouds, this is achieved by using the cloud storage services, which are unable to exploit the workflow semantics and are subject to low throughput and high latencies. To overcome these limitations, we propose an alternative leveraging data locality through direct file transfers between the compute nodes. We rely on the observation that workflows generate a set of common data access patterns that our solution exploits in conjunction with context information to self-adapt, choose the most adequate transfer protocol and expose the data layout within the virtual machines to the workflow engines. This file management system was integrated within the Microsoft Generic Worker workflow engine and was validated using synthetic benchmarks and a real-life application on the Azure cloud. The results show it can bring significant performance gains: up to 5x file transfer speedup compared to solutions based on standard cloud storage and over 25% application timespan reduction compared to Hadoop on Azure.