## 1 Introduction  
The mountains exemplify the distinctive geography of southeast China, characterized by intricate and diverse topography, substantial elevations, and a rich array of geographical features and meteorological variations. It encompasses the middle and lower reaches of the Yangtze River in China as well as the surrounding mountainous and hilly areas. The different heights and slopes of the mountainous terrains also contribute to the diverse climatic variations, with substantial differences in meteorological variables among the mountain ranges [1]. In order to gain a deeper understanding of the vertical structure of the atmosphere and the pattern of meteorological changes in the mountainous regions of southeast China, accurate meteorological data are essential. ERA5 (5 th Generation European Reanalysis Datasets) is a global reanalysis dataset developed by the ECWMF [2; 3]. Through integrating a wide range of observational data and advanced numerical weather prediction model outputs, ERA5 provides continuous and comprehensive vertical coverage of meteorological variables from 1000 to 1 hPa. It has been employed in multiple fields. [PERSON] et al. (2023) found, through analyzing ERA5 data, that thunderstorm straight line winds are enhanced with climate change [4]. [PERSON] et al. (2023) assessed the applicability of ERA5 wind and wave products in the South China Sea and found that the bias between the ERA5 and observed significant wave height (SWH) data varied from \(-0.24\) to \(0.28\) m. The ERA5 data showed a positive SWH bias, which implied a general underestimation at all locations, except those in the Beibu Gulf and central-western SCS, where overestimation was observed [5]. [PERSON] et al. (2023) evaluated the global warming levels due to different emission scenarios using the recent (1950-2019) and future projections of ERA5 [6]. [PERSON] et al. (2021) found that ERA5 performs best in both the rainy and dry seasons for the coastal urban and mountainous regions of the Guangdong-Hong Kong-Macao Greater Bay Area, China. ERA5 is not sufficient to reproduce precipitation in highly urbanized areas, and the model obtained for ERA5 precipitation on the monthly scale agrees well with the actual model, with a spatial correlation coefficient of more than 0.75, while it falls below 0.5 on the daily and hourly scales [7]. ERA5 is able to support the predictions of global warming levels over China without affecting the diel cycle [6]. The ERA5 datasets has high spatial and hourly temporal resolutions, and although the spatial resolution of the ERA5 data is relatively high, the spatial distribution of the data may not be well enough in some regions, especially in mountainous plateaus and coastal areas with complex topography [8; 9; 10]. This may lead to certain errors and biases in meteorological and climatic characteristics in these regions. There have been many assessments of the applicability of ERA5 reanalysis data [11; 12]. [PERSON] et al. (2018) conducted a preliminary assessment of the applicability of the ERA5 reanalysis data by using the surface and elevated data from 10 stations in Shandong province and the surrounding areas. The mean absolute deviation of the temperature field below 300 hPa ranged between 0.4 and 0.6 K, and gradually increased above 300 hPa, as did the relative humidity [13]. [PERSON] et al. (2013) investigated the applicability of the high-altitude temperature field in various reanalysis data in China by combining with the sounding data and found that ERA5 high-altitude temperature data are better described in the upper troposphere in the northern part of China [14]. [PERSON] et al. (2019) evaluated the ERA5 reanalysis precipitable water vapor datasets based on Central Asian radiosonde observation pairs [15]. They suggest that ERA5 can be used to understand the interannual variations of PWV in central Asia.