Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals

Accurate projections of future surface solar radiation (SSR) are important for assessing the impacts of climate change and the potential of solar energy. However, climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibit notable uncertainties in SSR projections. This study aims to develop a high quality monthly SSR dataset during 1850–2100 by synthesizing CMIP6 model projections and satellite-derived retrievals using a Bayesian Linear Regression (BLR) method. Five CMIP6 models are selected based on their historical performance in simulating SSR. The BLR method assigns gridded weights to each model based on how well the historical simulations matched the satellite-based SSR product (called ISCCP‒ITP‒CNN) over the period 1983–2014. The weighted multi-model ensemble is calculated to generate a synthesized long-term SSR dataset. Evaluation against ground-based observations during historical periods (1960–2017) shows that the synthesized SSR outperforms individual CMIP6 models and their original multi-model mean, with a reduced RMSE from 32 to 36 W/m 2 to 25 W/m 2 and a bias from 5 to 13 W/m 2 to −1 W/m 2 on monthly scales. The spatial patterns also agree well with the ISCCP‒ITP‒CNN (1983–2018). The high-resolution (0.1° × 0.1°) synthesized SSR dataset provides monthly projections over historical experiments and four future shared socio-economic pathway (SSP) scenarios (SSP126, SSP245, SSP370, and SSP585) during 1850–2100, representing future SSR changes and associated climate impacts. The dataset is expected to enhance simulations of land surface processes and solar energy applications under a variety of future climate scenarios.