Abstract: An interpolation algorithm for radar reflectivity data is proposed using the Gaussian-scale mixtures (GSM) in the wavelet domain as the prior model, which can accurately describe the statistical characteristics of radar precipitation reflectivity data. The objective is to improve the radar image resolution while effectively reproduce those important spatial statistical characteristics of the precipitation echoes like local extreme intensity values and small scale variation gradient. Firstly, the statistical characteristics of radar precipitation reflectivity data in the wavelet domain are analyzed and the reflectivity data are modeled with the GSM. Then, the wavelet coefficients of the radar reflectivity data are matched with the GSM in the wavelet domain, and the wavelet coefficients at smaller scale are estimated by Bayesian theory. The high resolution radar reflectivity image can be recovered from inverse wavelet transform of the estimated coefficients at smaller scale. The case study shows that the proposed algorithm can get the high frequency coefficients of the high resolution images through the parameters estimations of low resolution images and the model used considers the statistical characteristics of precipitation reflectivity data; the interpolation result can capture the non-Gaussian singularities and local correlated features of the precipitation echoes, and the local details of the high resolution radar reflectivity images can be well reproduced.