Zonation refers to the spatially constrained clustering of objects of interest with location information based on the similarity of their attributes. The results of zonation by clustering are usually relatively homogeneous spatial units in raster or vector formats. The spatial distribution of tropical cyclone (TC) hazards, such as TC wind and rainfall, may result in significant spatial heterogeneity from coastal to inland areas, and proper spatial zonation can greatly improve the understanding and management of TC risks. Although zonation methods have been developed based on expert knowledge, simple statistics or GIS tools in past studies, various challenges still exist in the areas of selecting representative attribute indicators, clustering algorithms, and fusion of multiple indicators into an integrated scaling indicator. In this study, TC hazards are chosen to explore methods for zonation and scaling. First, wind data of 1,256 TCs from 1949 to 2017 and rainfall data of 895 TCs from 1951 to 2014 were collected at a 1-km resolution. The mean, standard deviation, and intensity of the 200-year return period for wind and rainfall were estimated and used as representative hazard intensity indicators (HIIs) for spatial clustering. Second, the K-means, interactive self-organizing data analysis techniques algorithm, mean shift and Gaussian mixture model were used to test the suitability of natural hazard zonation based on raster data. All four algorithms were found to perform well, with K-means ranking the best. Third, a hierarchical clustering algorithm was utilized to cluster the HIIs into polygons at the provincial, city and county levels in China. Finally, the six HIIs were weighted into a single indicator for integrated hazard intensity scaling. The zonation and scaling maps developed in the present study can reflect the spatial pattern of TC hazard intensity satisfactorily. In general, the TC hazard scale is decreasing from the southeast coast to the northwest inland of China. The methods and steps proposed in this study can also be applied in the zonation and scaling of other types of disasters as well.