Continuous high-resolution spatial ten-day precipitation data are essential for crop growth services and phenological research. In this study, we first use the bidimensional empirical mode decomposition (BEMD) algorithm to decompose the digital elevation model (DEM) data and obtain the terrain decomposition results of the high-frequency margin terrain (OR3), intermediate-frequency margin terrain (OR5) and low-frequency margin terrain (OR8) models. Then, we use ground-based meteorological observation data, integrated multi-satellite retrievals for global precipitation measurement (GPM IMERG) satellite precipitation products, DEM data, terrain decomposition data, prevailing precipitation direction (PPD) data and other multisource data to build a ten-day precipitation spatial model and construct China's ten-day precipitation spatial dataset from 2001 to 2018. The decomposition results show mountainous terrain from fine to coarse; altitude, slope, and aspect influence precipitation based on large topographic features after the topography is decomposed; terrain decomposition data can be added to the simulation to improve the quality of the simulation product; the simulation quality of the constructed model in summer is better than that in spring and autumn, and the simulation quality in winter is relatively poor; and OR5 and OR8 can be improved in the simulation, in which the better of OR5 and OR8 is dynamically selected. In addition, preprocessing the data before precipitation spatialization is particularly important. For example, adding 0.01 to the 0 value of precipitation, multiplying the smaller value of precipitation less than 1 by 10, and performing the normal distributions transform (NDT) on the data can improve the simulation quality.
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