Extreme event-based synchronicity is a specific measure of similarity of extreme event-like time series. It is capable to capture the nonlinear interactions between climatic extreme events. In this study, we proposed a modified extreme event-based synchronicity measure that incorporates two types of extreme events (positive and negative) simultaneously in climate anomalies to characterize the synchronization and time delays. Statistical significance of the modified extreme event synchronization measure is tested by Monte-Carlo simulations. The applications of the modified extreme event-based synchronicity measure on synthetic time series verified that it was superior to the traditional event synchronicity measure. Both synchronous and antisynchronous features between climate time series could be captured by the modified measure. It is potentially applied in investigating the interrelationship between climate extremes and climate index or constructing complex networks of climate variables. In addition, this modified extreme event-based synchronicity measure could be easily applied to other types of time series just by identifying the extreme events properly.