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The traditional meteorological data estimation is mostly based on the interpolation methods, which require the complete observation data of the nearest neighbor stations and largely limit the application of the interpolation methods. This paper proposes a method for estimating meteorological data based on matrix completion. The proposed method estimates missing data based on the approximate lowrankness of meteorological data. The daily average temperature and sunshine hours of 662 meteorological stations in China from 2004 to 2013 are selected as the research objects. The approximate lowrankness of the data set is validated by the cumulative contribution rate of matrix singular values. Then two groups of experiments are designed. The first group of experiments considers the data estimation for different years with different sampling probabilities. The second group randomly chooses some stations and considers the data estimation when the data of the selected stations are continuously missing. Finally, the matrix completion method is employed to estimate the missing data, and the 10year average error is used as the evaluation index. Experimental results show that the matrix completion method has good estimation performance and certain robustness.