AbstractExponentially weighted moving average (EWMA) control chart has a significant effect in improving product quality and is widely used in various fields. In addition to continuous data, there are many Count Data in life that need to be monitored. Poisson distribution is one of the models that study the probability distribution of discrete data, and has a wide range of applications. In previous monitoring, it was considered that the mean value of Poisson distribution in normal state was a constant value after it was determined. But in the actual situation, there are many unavoidable objective conditions that will affect the final results. We cannot monitor all situations according to the same criteria. If we ignore the conditions that affect the occurrence of the event and directly monitor the final result, on the one hand, it will increase the probability of false alarms from the control chart. On the other hand, the control chart will not be able to detect problems in time due to the untimely update of conditions. In response to this situation, this paper proposes a regression‐adjusted EWMA control chart to monitor the Poisson process. The control chart can continuously adjust and update the expected values according to the actual situation. It can make the monitoring process more reasonable and the monitoring results more valuable.