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Abstract The raindrop size distribution (DSD) characteristics during the precipitation season are analyzed using data collected by an OTT Particle Size Velocity (Parsivel) disdrometer and a Vertical Pointing Radar with C-band Frequency Modulation Continuous Wave (VPR-CFMCW) technology. The two datasets were collected at the same site in Longmen, Guangdong Province, which is the precipitation center of south China, from June to July in 2016 and 2017. We evaluate different fitting methods for the gamma model function and choose a nonlinear least-squares method to fit DSD. Based on the radar reflectance obtained by VPR-CFMCW, the precipitating clouds that occur during the summer precipitation season in south China are classified into four types (i.e., convective, stratiform, mixture, and shallow). The characteristic parameters and the gamma model parameters of different precipitation types are compared. Avoiding the limitations of rainfall classification at the surface, the new classification quantifies the characteristics of mixture and shallow precipitation. The results show that the stratiform precipitation makes up 43.1% of the summer precipitation process in south China, and the contribution of convective precipitation to total rainfall is 62.7%. The precipitation parameters of the four types of precipitation, such as the rain rate (R), the mass-weighted mean diameter (D m), the radar reflectance (Z), and the liquid water content (LWC), follow the pattern: convective > mixture > stratiform > shallow. The DSD characteristics of the four precipitating cloud types are investigated. For the DSD of convective and mixture precipitation, the spectra width is similar but the rain drop concentration of the mixture is smaller. For the DSD of stratiform and shallow clouds, the rain drop concentrations are similar, but the spectra width of the shallow clouds are smaller. In addition, the relationships between μ-Λ, D m -N w , D m -R, and Z -R are obtained. These new relationships will help improve the accuracy of precipitation estimation and deepen the understanding of the characteristics of surface precipitation microphysical parameters for different types of precipitating clouds in south China. Highlights • The surface precipitation is associated with the high altitude precipitation cloud structure, the application of vertical detection radar enhances the study of precipitation. The results show the particularity of precipitation in south China. • Based on the radar reflectance obtained by VPR-CFMCW, the precipitation clouds that occur during the summer precipitation season in south China are classified into four types (i.e., convection, stratiform, mixture, and shallow). • New relationships will help improve the accuracy of precipitation estimation and deepen the understanding of the characteristics of surface precipitation microphysical parameters for different types of precipitation clouds in south China. • Different methods of DSD gamma fitting were evaluated. [ABSTRACT FROM AUTHOR]

This paper investigates the nonstationarity of precipitation extremes by incorporating time-varying and physical-based explanatory covariates, using daily precipitation data across the Beijing-Tianjin-Hebei (BTH) region, China. We perform the stationary and nonstationary generalized extreme value (GEV) models based on the Bayesian framework to estimate the expected return levels of precipitation extremes with the 90% credible intervals. Results reveal that the nonstationarity of precipitation extremes is not prominently visible for the majority of sites in BTH. However, the nonstationary GEV models exhibit better performance to capture the variations of precipitation extremes by comparison to the stationary models based on four evaluation criteria. Further, this work attempts to determine the best covariate to illustrate the possible effects of environmental changes on the frequency analysis. Results indicate that the El Nino-Southern Oscillation (ENSO) is the top of the best covariates, followed by the East Asian summer monsoon, North Atlantic Oscillation (NAO) and local temperature anomaly. Moreover, the best covariates are dominated by the physical-based covariates, and the best models with nonlinear functions of covariates are found in the majority of sites. Finally, the best-fitted models are used to estimate the design values of return levels in precipitation extremes. Results illustrate that the differences between the stationary modeling and nonstationary modeling in the median condition of covariates are not significant for most of the sites. But the discrepancies will be enhanced if the covariates locate in a high (95-percentile) or low (5-percentile) value. Our findings suggest that the nonstationary modeling of precipitation extremes might prove more useful and reliable, especially in the uncommon conditions of physical-based covariates. Unlabelled Image • Nonstationary frequency analysis can better capture the temporal evolution of precipitation extremes • The best covariates for nonstationary modeling of precipitation extremes are dominated by the physical-based covariates • Higher return period indicates higher uncertainty in expected return levels both for stationary and nonstationary models [ABSTRACT FROM AUTHOR]