Abstract: Data quality control is a basic assurance for meteorological researches and data applications. In order to efficiently use the data sets from AWS (Automatic Weather Station) constructed in 1998, hourly data (including precipitation, relative humidity, air pressure, wind speed and direction) measured by 187 AWS over Beijing from 1998 to 2010 were evaluated by its integrality, veracity and confidence in the paper. The approach included the definition and principle applied in the temperature data assessment, in which the AWS data can be divided into correct, severe missing, missing, discrete and slight continuous missing, and dubious data. The spatial and temporal consistent detections are employed in the data quality control fl ow. The results show as follows: The AWS net of Beijing was set up following a fi ne layout. During the AWS construction process, the regional character was considered for the representation of AWS. Even in the beginning period, the AWS were set up in urban areas and mountainous areas as well according to the need of different region representation. It is beneficial to urban and rural climate comparison, data sequence reconstruction and regional climate research. The collectivity of the fi ve kinds AWS data is fi ne. The severe and bad missing data was few. The dispersing and lightly consecutive missing data were concentrated with regional consistency. It is shown from the error test that the incidence of mistakes were different among the fi ve kinds data. Although the number of extreme errors in precipitation is higher than other error styles, the highest number of cumulative errors of precipitation data is not beyond 43 (2005). Relative humidity and air pressure, had errors almost simultaneously. The results of suspicious data discriminating revealed that the suspicious data of relative humidity had the highest proportion in 2003 and 1999, after 2004, while the suspicious data of pressure, which failed, in space consistency check occupy a larger proportion in 2004. The cause remains for further study.