Climate change is a severe problem caused by abnormal climate events. The existing methods for detecting climate changes utilize statistical models to analyze the atmospheric temperature, but a climate event commonly comprises multiple meteorological data. To detect climate changes using meteorological data, we propose a novel dynamic graph embedding model based on graph entropy called EDynGE. A climate event is denoted as a graph, in which the nodes indicate meteorological data and edges indicate the correlation between nodes. Graph entropy measures the information of the climate event, and the EDynGE model clusters graphs based on graph entropy. We conducted experiments on real meteorological data. The results showed that the number of days of abnormal climate events has increased by 304.5 days in the past 30 years.