Temporal graph neural network (GNN) has recently received significant attention due to its wide application scenarios, such as bioinformatics, knowledge graphs, and social networks. There are some temporal GNNs that achieve remarkable results. However, these works focus on future event prediction and are performed under the assumption that all historical events are observable. In real-world applications, events are not always observable, and estimating event time is as important as predicting future events. In this article, we propose, a missing event-aware temporal GNN, which uniformly models evolving graph structure and timing of events to support predicting what will happen in the future and when it will happen. models the dynamic of both observed and missing events as two coupled temporal point processes (TPPs), thereby incorporating the effects of missing events into the network. Experimental results on several real-world temporal graphs demonstrate that significantly outperforms the existing methods with up to 89% and 112% more accurate time and link prediction. Code can be found on https://github.com/HIT-ICES/TNNLS-MTGN.