Abstract: Planetary albedo is defined as the ratio of the reflected and incident shortwave solar radiation at the top of the atmosphere, and it is also a critical parameter for the surface energy budget and global climate change. To improve our understanding of the spatiotemporal characteristics of the global planetary albedo, this work decomposed the atmospheric and surface contributions of the global planetary albedo using the Clouds and the Earth’s Radiant Energy System data, derived the global planetary albedo trends from 2001 to 2018 using the Theil-Sen+Mann-Kendall method, and explored the driving factors of the planetary albedo in typical regions by the correlation analysis method. Results showed that: 1) In mid-low latitudes (<60°), the atmospheric contribution is the dominant factor to the planetary albedo (89.3%±5%), while in high latitudes (>60°), the surface contribution to the planetary albedo increases with latitude (29%±12%). The spatial distribution of the planetary albedo showed that the zonal variations of the planetary albedo were larger than the meridional variations. 2) A decreasing trend of −0.0002/a can be observed in the global planetary albedo from 2001 to 2018, and decreasing trends of −0.00015/a and −0.00004/a can be found in the surface and atmospheric contribution to the planetary albedo from 2001 to 2018, respectively. The decrease of the global planetary albedo can be largely explained by the decrease of the global cloud fraction. 3) For the atmospheric contribution to the planetary albedo, significant increasing trends can be found in regions of the Sahara Desert and other deserts, and significant decreasing trends can be found in the eastern Antarctic and other locations. For the surface contribution to the planetary albedo, significant increasing trends can be found in eastern Antarctica and other locations, and significant decreasing trends can be found in the Arctic Ocean and other locations. Moreover, the variation of the planetary albedo in typical regions can be effectively explained by the variation of the cloud fraction, snow cover, and the normalized difference vegetation index.