Downward surface solar radiation (Rs) plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of Rs are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, so it is essential to build a long-term accurate Rs dataset. The extremely randomized trees (ERT) algorithm was used to generate Rs using routine meteorological observations (2000-2015) from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA). The estimated Rs values were validated against ground measurements at the national scale with an overall correlation coefficient value of 0.97, a mean bias of 0.04 Wm-2, a root-mean-square-error value of 23.12 Wm-2, and a mean relative error of 9.81%. It indicates that the estimated Rs from the ERT-based model is reasonably accurate. Moreover, the ERT-based model was used to generate a new daily Rs dataset at 756 CDC/CMA stations from 1958 to 2015. The long-term variation trends of Rs at 454 stations covering 46 consecutive years (1970-2015) were also analyzed. The Rs in China showed a significant decline trend (-1.1 Wm-2 per decade) during 1970-2015. A decreasing trend (-2.8 Wm-2 per decade) in Rs during 1970-1992 was observed, followed by a recovery trend (0.23 Wm-2 per decade) during 1992-2015. The recovery trends at individual stations were found at 233 out of 454 stations during 1970-2015, which were mainly located in southern and northern China. The new Rs dataset would substantially provide basic data for the related studies in agriculture, ecology, and meteorology.
Keywords: downward surface shortwave radiation; extremely randomized trees; global brightening; global dimming; random forest.