Both cities and lakes have significant impacts on regional precipitation. With global warming, extreme precipitation events in Eastern China have increased significantly, and the single/joint influences of metropolises and lakes on extreme precipitation still need to be quantitatively evaluated. To reveal the impact of the single/joint influences of metropolises and lakes on the shear line torrential rain process, the Suzhou-Wuxi-Changzhou Metropolitan Area (SXCMA) and Lake Taihu in Eastern China were selected as the study area. Utilizing a WRF model, comparative studies of sensitivity simulations were conducted for the two typical extreme precipitation events caused by the low-level shear line (LLSL) on 27 June 2015 (EP627) and 25 September 2017 (EP925). Both results show that the existence of Lake Taihu and SXCMA will increase precipitation in the study area. SXCMA has a more obvious effect on enhancing precipitation, which is about twice the effect of Lake Taihu. SXCMA mainly strengthens the intensity and movement of the surface convergence line (SCL) in the study area and indirectly affects the shift of the LLSL, which finally affects the intensity and location of precipitation. Lake Taihu affects the intensity and movement of SCL, triggering ground vertical convections due to lower surface roughness, and acts as a land-lake breeze and water vapor source, which will affect the distribution and intensity of precipitation.

Land-surface characteristics (LSCs) and land-soil moisture conditions can modulate energy partition at the land surface, impact near-surface atmosphere conditions, and further affect land–atmosphere interactions. This study investigates the effect of land-surface-characteristic parameters (LSCPs) including albedo, leaf-area index (LAI), and soil moisture (SM) on hot weather by in East China using the numerical model. Simulations using the Weather Research and Forecasting (WRF) Model were conducted for a hot weather event with a high spatial resolution of 1 km in domain 3 by using ERA-Interim forcing fields on 20 July 2017 until 16:00 UTC on 25 July 2017. The satellite-based albedo and LAI, and assimilation-based soil-moisture data of high temporal–spatial resolution, which are more accurate to match fine weather forecasts and high-resolution simulations, were used to update the default LSCPs. A control simulation with the default LSCPs (WRF_CTL), a main sensitivity simulation with the updated LSCP albedo, LAI and SM (WRF_CHAR), and a series of other sensitivity simulations with one or two updated LSCPs were performed. Results show that WRF_CTL could reproduce the spatial distribution of hot weather, but overestimated air temperature (Ta) and maximal air temperature (Tamax) with a warming bias of 1.05 and 1.32 °C, respectively. However, the WRF_CHAR simulation reduced the warming bias, and improved the simulated Ta and Tamax with reducing relative biases of 33.08% and 29.24%, respectively. Compared to the WRF_CTL, WRF_CHAR presented a negative sensible heat-flux difference, positive latent heat flux, and net radiation difference of the area average. LSCPs modulated the partition of available land-surface energy and then changed the air temperature. On the basis of statistical-correlation analysis, the soil moisture of the top 10 cm is the main factor to improve warming bias on hot weather in East China.