This study explored city residents’ collective geo-tagged behaviors in response to rainstorms using the number of location request (NLR) data generated by smartphone users. We examined the rainstorms, flooding, NLR anomalies, as well as the associations among them in eight selected cities across the mainland China. The time series NLR clearly reflects cities’ general diurnal rhythm and the total NLR is moderately correlated with the total city population. Anomalies of NLR were identified at both the city and grid scale using the S-H-ESD method. Analysis results manifested that the NLR anomalies at the city and grid levels are well associated with rainstorms, indicating city residents request more location-based services (e.g. map navigation, car hailing, food delivery, etc.) when there is a rainstorm. However, sensitivity of the city residents’ collective geo-tagged behaviors in response to rainstorms varies in different cities as shown by different peak rainfall intensity thresholds. Significant high peak rainfall intensity tends to trigger city flooding, which lead to increased location-based requests as shown by positive anomalies on the time series NLR.