  Coarse graining model is a promising way to analyze and visualize large-scale
networks. The coarse-grained networks are required to preserve the same
statistical properties as well as the dynamic behaviors as the initial
networks. Some methods have been proposed and found effective in undirected
networks, while the study on coarse graining in directed networks lacks of
consideration. In this paper, we proposed a Topology-aware Coarse Graining
(TCG) method to coarse grain the directed networks. Performing the linear
stability analysis of synchronization and numerical simulation of the Kuramoto
model on four kinds of directed networks, including tree-like networks and
variants of Barab\'{a}si-Albert networks, Watts-Strogatz networks and
Erd\"{o}s-R\'{e}nyi networks, we find our method can effectively preserve the
network synchronizability.
