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In order to enhance the daily application of high-resolution new detection data and improve the analysis and prediction abilities of forecasters for weak precipitation weather processes, at the same time, for the sake of developing superior meteorological support capacities for important activities or major events, new detection data such as microwave radiometer, laser wind radar, Doppler radar, phased array radar, ground densified observation data, ERA5 reanalysis data, and multimodel numerical prediction data are used to analyse a forecast error that occurred during a weak precipitation event in Xi’an urban area on 24 April 2022. The result shows that (1) Both the global numerical models (CMA-GFS, ECMWF, NCEP) and mesoscale numerical models (CMA-Beijing, CMA-MESO, CMA-TYM) predicted significant precipitation in Xi’an urban area, mainly due to the high prediction of low-level relative humidity. (2) Based on the analysis of various new types of detection data, poor humidity conditions in the near-surface layer and low absolute water vapour content in the middle layer were found, leading to the conclusion that dry and warm air in the middle layer could not be conducive to cloud formation and rainfall. Moreover, weak vertical upward movement with very dry lower layers caused the raindrops to evaporate during the falling process, making it impossible for raindrops to fall to the ground in Xi’an urban area. (3) The reason for the poor humidity conditions in the near-surface layer of the urban area of Xi’an was the urban dry heat island effect and the low-level dry warm advection transport. Moreover, after the precipitation cloud group climbed the Qinling Mountains, its humid air adiabatically sank to the urban area, causing an increase in temperature and a decrease in humidity, resulting in the formation of a relatively deep dry layer in the urban area. Even if there were raindrops falling, stronger evaporation would occur due to the above conditions, which was also one of the important reasons for the lack of precipitation in the urban area of Xi’an. (4) The main reason for the lack of successful subjective prediction correction ability was that forecasters lacked the skills to revise key precipitation elements such as boundary layer water vapour, dynamic uplift conditions, or some other elements that were beneficial for precipitation. Furthermore, there was insufficient research on the dry heat island effect of big cities like Xi’an or the terrain impact on weak precipitation in the Qinling Mountains.