To reduce the harmful effects of rain for visual tasks， rain removal algorithms are commonly utilized on single frame images or video streams to remove rain. However， since rain falls extremely fast， frame-based cameras cannot capture the temporal continuity of rain， and the fixed exposure time and motion blur further reduce the sharpness of the rain on images， as a result， the traditional image rain removal algorithms cannot detect rain coverage areas accurately. In order to explore the new idea of image rain removal， a rain event generation model was constructed and a rain detection algorithm for event camera based on spatial-temporal relevance was proposed by using the characteristics of event camera： extremely high sampling rate and no motion blur. In this algorithm， the probability of each event generated by rain movement was calculated by analyzing the spatial-temporal relationship between each event recorded by the event camera and adjacent events， so as to achieve rain detection. Experimental results on three rainfall scenes show that when the camera is static， the proposed algorithm can reach more than 95% rain detection true positive rate， and the false positive rate less than 5%， and when the camera moves， the proposed algorithm can still reach more than 95% true positive rate and no more than 20% false positive rate. The above shows that the rain can be detected effectively by the proposed algorithm.