In order to explore the spatial interpolation method suitable for commercial microwave link rainfall monitoring data, the inverse distance weight method, Kriging method and trend surface method were analyzed based on the empirical model of wireless microwave rain decay characteristics when different linear rainfall data were converted to point-like rainfall data. The results show that the denser and more microwave links are, the better the trend consistency is. In the five scenarios of converting the microwave linear data to the point data, the spatial interpolation effect is better when the microwave link is generalized to the specific rainfall monitoring points at the interval of 200 m. The spatial interpolation effect of trend surface method is better, the inverse distance weight method is second, and the Kriging method is the worst. The research results clarify the principle of converting linear rainfall data into grid spatial data, provide support for constructing spatial two-dimensional rainfall fields in commercial microwave link networking, and have important significance for improving the compatibility of linear rainfall monitoring products.