大气可降水量（PWV）是表征大气中水汽含量的重要指示因子，明确PWV与降水的转化机制对水资源高效利用具有重要意义。本文以新疆为研究区，基于多源数据计算PWV，并以探空数据为参考，评估ERA5全球大气再分析数据计算PWV的优劣，借助降水转换率（PCE）揭示PWV与降水的转化关系。结果表明：（1）ERA5计算的PWV精度较高，与传统依托探空数据确定的PWV的相关系数和均方根误差分别为0.98和2.6 mm。（2）1960—2020年新疆PWV总体呈增加趋势，增幅为0.1 mm·(10a)^-1；小波频谱显示研究区PWV变化周期以短周期为主，分别为2.6 a、6 a。（3）从点尺度来看，PCE随站点降水量的增加而升高；从线尺度来看，PCE的变化规律在经度方向上呈“U”型，在纬度方向上大致呈“L”型；从面尺度来看，PCE的高值区主要分布在林地、坡度为25°~35°范围及海拔5000 m以上的区域，分别为7.17%、5.8%和5.1%；（4）降水丰枯异常典型年PCE差异明显，辐合强、水汽上升运动强烈的特丰水年，引起了较高的PCE，平、枯水年PCE较低。（5）北极涛动指数和太平洋十年振荡是影响全疆PCE的主要因素，由于不同区域间因气候和地形等因素的差异，PCE受控因子存在一定的差异。研究结果可为新疆地区空中水资源利用及降水转化评估提供理论参考。

Atmospheric Precipitable Water Vapor (PWV) is an important indicator to characterize the water vapor content in the atmosphere, and clarifying the conversion mechanism between PWV and precipitation is of great significance for efficient water resource utilization. This paper takes Xinjiang as the study area, calculates PWV based on multi-source data, and evaluates the advantages and disadvantages of ERA5 global atmospheric reanalysis data for calculating PWV using radiosounde data as reference, and reveals the conversion relationship between PWV and precipitation with the help of Precipitation Conversion Efficient (PCE). The results show that (1) The PWV calculated by ERA5 has a high accuracy, with correlation coefficients and root mean square errors of 0.98 and 2.6 mm, respectively, compared to the PWV determined by traditional radiosounde data dependent methods. (2) Overall increasing trend of PWV in Xinjiang from 1960-2020, with an increase of 0.1 mm·(10a)^-1; the wavelet spectrum shows that the period of PWV change in the study area is dominated by the short period, which is 2.6 a and 6 a, respectively. (3) From a point perspective, PCE increases with increasing precipitation at the station. From a line perspective, the pattern of change in PCE is “U” type in the direction of longitude and roughly “L” type in the direction of latitude. At the surface scale, the high value areas of PCE were mainly distributed in the forested land, the slope range of 25°-35° and the area above 5000 m above sea level, which were 7.17%, 5.8% and 5.1%, respectively. (4) Typical years of precipitation abundance anomalies vary significantly in PCE, with exceptionally abundant years with strong convergence and strong upward movement of water vapor giving rise to higher PCE, and flat and dry water years with lower PCE. (5) Arctic Oscillation Index and Pacific Decadal Oscillation are the main factors affecting the PCE in the whole of Xinjiang, and there are some differences in the PCE controlled factors among different regions due to differences in climate and topography. The results of the study can provide theoretical references for airborne water resource utilisation and precipitation conversion assessment in Xinjiang.