## 1 Introduction
Thus, [44] reported that over the period January 2001-December 2015 the mean global surface solar radiation from MERRA-2 is equal to 185.6 Wm\({}^{-2}\), while for the same period, according to [45], JRA-55 yields 189 Wm\({}^{-2}\), ERA5 187.9 Wm\({}^{-2}\), NOAA-CIRES-DOE gives 192.9 Wm\({}^{-2}\) and from NCEP-NCARSSR is equal to 205.3 Wm\({}^{-2}\). Nevertheless, land surface data obtained from reanalyses have problems caused by inhomogeneities in the assimilated data records that obtained primarily from atmospheric profiles, affecting the near-surface meteorology. These errors, along with others, like those related to clouds, aerosols, water vapor, topography and surface albedo, can cause systematic biases in reanalysis SSR products [39,46]. Indeed, most reanalyses (including MERRA and MERRA-2) showed average positive biases [39,47], and a strong overestimation of SSR over regions such as Europe, Asia and North America. This positive bias was found to be due to an underestimation of the cloud fraction [39,48]. In addition, the study by [49] revealed that MERRA and ERA-Interim reanalyses often predict clear sky conditions, while actual conditions are cloudy, yielding an overestimation of SSR, with the opposite, namely actual clear-sky conditions predicted by reanalyses as cloudy, yielding an underestimation of SSR, being less pronounced. The same finding was reported by [50] over the tropical Atlantic Ocean using MERRA-2 and ERA-5. Hence, there is a systematic effort to produce Reanalyses with improved quality products, e.g., MERRA-2 versus MERRA. Thus, [44] showed that MERRA-2 SSR represent better than MERRA the variability and trends in the global mean radiative fluxes over the period 2001-2015, however noting that the MERRA-2 radiative fluxes themselves are not clearly better than the MERRA ones, since both improvements and deterioration were found. Also, the same study proved that clouds are clearly a source of significant SSR errors in both MERRA and MERRA-2. Of course, these SSR uncertainties also make any Reanalysis-based GDB assessment uncertain. Therefore, it is evident that a thorough validation of not only the reanalysis SSR products, but also the associated SSR trends and GDB, against available reference ground measurements is necessary before being used in climate studies. Such evaluations have been made only for reanalysis SSR, and much less for SSR trends, as well as for its driving factors. Most studies have performed reanalyses' SSR evaluation against ground-based stations over several regions, such as Indonesia [51], Balochistan (Pakistan, [52]), Europe [53], Norway [54], East Antarctic Plateau [55], Yangtze River Delta [56],China [47] and references therein, East Asia [46], North America [57], Ireland [58], South Africa [59], Ethiopian Highlands [60], and Eurasia [61]. Besides, the ERA40 reanalysis SSR has been evaluated globally against ground-based GEBA (Global Energy Balance Archive) stations [62], the ERA5 and MERRA-2 SSR were evaluated against BSRN (Baseline Surface Radiation Network) stations [63], while the ERA-Interim, JRA-55 and NCEP-DOE SSR were validated against GEBA and CMA (China Meteorological Administration) stations [64]. Much fewer studies deal with the assessment even fewer with the evaluation of SSR trends. The study by [38] evaluated the SSR of MERRA-2 reanalysis against ground-based (GEBA and CMA) stations. For regions over which good SSR evaluation metrics, they also investigated the SSR long-term trends from 1980 to 2019 and estimated and examined the corresponding trends of clouds and aerosols. They found that during 1980-2019, there has been a widespread decline of SSR (dimming) in North America, Northeast Asia and Oceania, equal to \(-0.32\), \(-0.00\) and \(-1.14\) Wm\({}^{-2}\)decade\({}^{-1}\), respectively, and an increase of \(0.63\) Wm\({}^{-2}\)decade\({}^{-1}\) over Europe.