Extreme precipitation events in arid areas often lead to huge casualties and economic losses, the study on its evolution characteristics and formation mechanism can provide an important support for improving the accuracy of weather forecast. A rainstorm process occurred on 13 August 2022 in Jinta County of Gansu Province, which was located in arid region of Northwest China. Both daily precipitation and hourly precipitation broke through the historical extreme value at national meteorological station Hexi Corridor, and their extreme and local characteristics were significant. European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation global atmospheric reanalysis (ERA5) and observation data were used in this paper to analyze the causes of the rainstorm. The results show that the rainstorm occurred in the north side of the stable South Asia high, and the dynamic forcing in the upper and middle level of troposphere was weak. The baroclinic system, the lower level shear line and surface cold front in front of the 500 hPa short-wave trough, was mainly located in the lower level. The continuous transport of low-level water vapor around the thermal over the Qinghai-Tibet Plateau provided the extreme water vapor condition and the moderate intensity stratification instability for the rainstorm area. In front of the formation of surface cold front, the regional difference of low-level water vapor transport in central and eastern parts of Jiuquan City formed an obvious wet frontal and dryline. The meso-γ-scale convective system which caused extreme short-term heavy precipitation was triggered by the dryline, and developed into deep moist convection leading to extreme heavy rain at the intersection point of the cold front and the dryline. The local characteristics were significant during the development of the dryline convective cells to deep moist convection.

Based on daily gas load and meteorological observation data during heating period in Xi’an of Shaanxi Province from 15 November 2009 to 14 March 2019, the variation characteristics of gas load in heating period, holidays and weekends were analyzed. The significant influence factors on gas load were selected by using correlation analysis. And on this basis the daily forecast model of gas load in heating period was established by using multiple linear regression method, then the forecast model was tested. The results show that the natural gas consumption during heating period gradually increased in Xi’an in past 10 years, the daily gas load presented a single-peak pattern change, and the peak appeared in January. The weekend and holidays effects of gas load were obvious during heating period, the gas consumption on weekend and holiday was less than that on working days, and the longer holiday was, the less gas load was. The gas load was significantly and positively correlated with gas load on previous day, while that was significantly and negatively correlated with meteorological factors of the maximum and minimum temperature, mean temperature and human body comfortable degree, and the correlation between heating gas load separated from actual gas load and meteorological factors obviously improved. Based on the above five influence factors, the dynamic forecast model of daily heating gas load was established by using multiple linear regression method. Upon inspection, the average relative error of the model was 3.4%, and the model was more stable in rush hours of using gas, the average relative error was 2.77%, which could meet gas dispatch needs of natural gas companies.