Abstract: Two new vorticity vectors (convective vorticity vector (CVV), defined as ) and moist vorticity vector (MVV), defined as )) introduced by Gao et al. (2004, 2005, 2007) are used to the study of a heavy rainfall event in North China. The result shows that vertical components of the CVV and the MVV are closely associated with clouds and precipitation. Analysis of domain-averaged and mass-integrated quantities shows that the linear correlation coefficients between the vertical component of the CVV (MVV) and the sum of mixing ratio of cloud hydrometeors is 0.92 (0.95), and that between the vertical component of the CVV (MVV) and precipitation rate is 0.71 (0.47). Cloud hydrometeors can be further broken into ice hydrometeors and water hydrometeors. Although ice hydrometeors are little, they contribute more to the evolution of clouds. Analysis of lag correlation suggests that minimum cloud hydrometeors leads the maximum precipitation rate by 4-5 hour, and maximum cloud hydrometeors lags maximum precipitation rate by 1-2 hour. The lag correlation coefficient between cloud hydrometeors and precipitation rate is mainly determined by the weighted lag correlation coefficient between water hydrometeors and surface rain rate, only one fourth of the lag correlation coefficient is contributed to the weighted lag correlation coefficients between ice hydrometeors and surface rain rate. Vertical components of CVV and MVV are well correlated with cloud hydrometeors (in phase or lag correlation) and precipitation rate, and they may represent the development of clouds and convective system. The local change of Cz and Mz lead surface rain rate by 3 hours, which may be a precursor for rainfall and has potential significance for rainfall prediction. The linear correlation coefficient between the local change of Cz and surface rain rate is larger than that between the local change of Mz and surface rain rate. Therefore, the local change of Cz can be used to estimate the change of surface rain rate in operational forecast.