Accurate prediction of global solar radiation (R-s) is important for understanding meteorological and hydrological processes, as well as the utilization of solar energy and development of clean production. In order to improve the accuracy and universality of daily R-s prediction in arid Northwest China, back-propagation neural network (BP) and BP optimized by the particle swarm optimization algorithm (PSO-BP) along with six statistical models (angstrom ngstrom-Prescott, Bristow-Campbell, Swartman-Ogunlade, Sebaii, Chen and Abdalla) were adopted and compared with measured R-s data from eight representative meteorological stations across four sub-climatic zones, including the temperate continental arid zone, temperate continental high temperature-arid zone, plateau continental semi-arid zone and temperate monsoon semi-arid zone. The results showed that PSO-BP models (coefficient of determination, R-2, 0.7649-0.9678) were more accurate than BP models (R-2, 0.7215-0.9632) and statistical models (R-2, 0.5630-0.9445) for the daily R-s prediction in the four sub-zones of arid Northwest China. The PSO-BP1 and BP1 models (with sunshine duration, maximum and minimum temperature, relative humidity and extraterrestrial radiation as inputs), PSO-BP2 and BP2 (with sunshine duration, maximum and minimum temperature and extraterrestrial radiation as inputs) performed better than the other models, with R-2, mean absolute error, root mean square error, relative root mean square error and Nash-Sutcliffe coefficient ranging 0.9228-0.9678, 1.5546-1.6309 MJ.m(-2).d(-1), 2.0054-1.7579 MJ.m(-2).d(-1), 0.1517-0.1329 and 0.9017-0.9604, respectively, among which the PSO-BP1 model provided the most accurate results. Sunshine-based models (R-2, 0.7533-0.9678) were generally superior to temperature-based models (R-2, 0.5630-0.8492), which indicated that sunshine duration was more influential for daily R-s prediction than temperature in this area. Overall, the PSO-BP model exhibits the best generalization capability and is recommended for more accurate daily R-s prediction in arid Northwest China.