Abstract: Climate change has led to an increased frequency of extreme rainfall events, posing a significant threat to urban water security. Assessing the spatial distribution of combined risk based on different precipitation characteristics can provide important scientific evidence for the prevention and mitigation of extreme rainfall disasters and urban flood control. This paper uses the 5-minute rainfall data from 56 rain gauges in the urban area of Kunming city from 2019 to 2021 as the basis, adopts the overdetermined method for precipitation event sampling, and calculates the rainfall duration D, peak rainfall P, and total rainfall V. A combined risk probability analysis model of precipitation characteristics is constructed by selecting optimal marginal distribution functions and Copula functions, which quantitatively assesses the combined risk probability of precipitation characteristics under different representative conditions and their spatial distribution characteristics. The results show that the variable D of each station in Kunming City mostly follows the Pearson Type III (PE3) distribution, variable V mostly follows the Log-Normal (Lognorm) distribution, and variable P mostly follows the Generalized Extreme Value (GEV) distribution, Generalized Logistic (GLO) distribution and Generalized Pareto (GPO) distribution; the peak rainfall P and rainfall duration D of all stations are negatively correlated or independent, all following the Frank Copula and Independent Copula functions. The Clayton Copula and Frank Copula functions can better describe the joint distribution of the variable combinations (P, V) and (D, V) for most stations. In the mountainous areas in the eastern part of Kunming’s main urban area, rainfall events with longer duration, higher peak rainfall, or greater total rainfall are likely to occur, while the central part of the main urban area is more prone to rainfall events with long duration, high peak rainfall, and large total rainfall.