中文摘要：

利用ERA-Interim提供的高空间分辨率（0.125°×0.125°）的大气可降水量和地表温度数据,采用经验正交函数分解、相关性分析和频谱分析等方法分析了青藏高原地区1979～2014年大气可降水量与地表温度的时空分布和变化特征及二者的变化关系.结果表明,在过去的36年里,青藏高原上空大气可降水量呈现微弱增多的趋势,地表温度呈现显著升高的趋势,二者的距平值变化一致.对大气可降水量和地表温度进行经验正交函数分解,发现大气可降水量和地表温度的第一特征向量场和第二特征向量场对应的时间系数显著相关,大气可降水量的第一特征向量场和地表温度的第四特征向量场之间的相关性高达0.9,两者和青藏高原地区的DEM（digital elevation model,DEM）的相关系数分别达到0.74和0.6,可以认为大气可降水量的第一特征向量场为大气可降水量的高程分量.同时,利用频谱分析方法分析大气可降水量和地表温度的时间序列数据,发现二者存在一致的约为3年的明显周期项.

英文摘要：

The Qinghai-Tibetan Plateau（TP）, also named ＂the Roof of the World＂, is known as the highest and largest plateau with an average altitude of about 4500 meters. Due to the unique orographical characteristics and thermal forcing mechanisms, the TP possesses unique plateau climate and, to a great extent, contributes to climate change over the whole world, especially East Asia. Intensive studies demonstrate that the warming pattern in TP exceeds that in other regions within the northern hemisphere and also the same latitudinal zone. In term of this result, the TP is regarded as one of the most sensitive regions to global climate change. However, the TP meteorological network is very sparse because of complex terrains and difficulties encountered in installing and maintaining the meteorological instruments. ERA-Interim has been a reanalysis of the global atmosphere covering the data-rich period since 1979, and is continuing in real time. ERA-Interim is the third-generation reanalysis product of European Centre for Medium-Range Weather Forecasts（ECMWF）, and it uses an improved data assimilation system and an improved forecast model compared with ERA-40. Investigations have shown that the ERA-Interim could well capture the temperature patterns and it is very reliable for climate change research. In terms of their spatiotemporal distribution and variation characteristics, this paper analyzes the ERA-Interim precipitable water vapor（PWV） and surface temperature products at the spatial resolution of 0.125°×0.125° during 1979 and 2014. Moreover, the relationship between PWV and surface temperature changes was also investigated, using the empirical Orthogonal Function decomposition（EOF） method, correlation analysis and spectrum analysis. First, the EOF method is used to characterize the dominant spatial pattern and compact the representation of PWV and surface temperature. As a popular analysis tool in climate research, the EOF method is maximally efficient in retaining as much information of the data