Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. This dataset provides the gridded surface global and diffuse solar radiation within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°, covering the period from 2007 to present. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The verification experiment in 2007 reveals that the root mean square error (RMSE) between our predictions and in-situ measurements is about 0.32 MJ/m^2^ (90 W/m^2^), 2.14 MJ/m^2^ and 1.30 MJ/m^2^ in hourly, daily and monthly scales, respectively. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.