## 4 Summary and Conclusions  
Anthropogenic emissions have been frequently studied and are known to influence climate significantly. However, the direct impacts of GHG and AER on land surface have not been sufficiently discussed. This study analyzed the data from the CMIP6 DAMIP experiment in an MLRM with ANOVA and provided variation maps of the land energy and water budgets, including GPP. In comparisons with ERA5 and FLUX-MTE data, PR, TAS, ET, SH, and GPP were confirmed to be reasonably estimated. Although the SSM contained an error and varied among the models, we assumed that CMIP6 SSM was appropriate for this study because the CMIP6 model has been improved from the CMIP5 model and the long-term SSM trend resembled that in a previous study [PERSON] et al. [32]. The MLRM analysis revealed the degree of each forcing. Unlike previous studies that typically focused on AER and GHG alone, we found that natural forcing also significantly influences meteorological and land surface variables. Normally, a long-term trend analysis  
Figure 9: All parameters obtained in the MLRM analysis. The variable names are followed by the number of ensemble models in parentheses. Orange shading indicates statistical significance. tends to focus on anthropogenic effects (AER and GHG) because of their clear effects. From natural-only simulation results, we cannot easily detect NAT contribution through a long-term time-series plot and even with some statistical analyses [9; 10]. In this study, by using MLRM parameter values, we showed that natural forcing can impact TAS and Net radiation as much as AER and GHG. Time-series figures showed NAT does not much influence long-term trends, and statistical significance of linear regression's slope parameter has not been detected in any NAT-only simulation. Rather, NAT tended to be related to high-frequency variations (e.g., volcanic eruption). Such contributions may not easily be detected without MLRM. The land surface process was mainly influenced by GHG and AER but with varying degrees of contribution. GHG increased key land surface variables (net radiation, ET, and runoff) along with PR and TAS, but AER presented opposite characteristics. Both GHG and AER continuously rose since 1850 (the beginning of the record). Their historical simulation (with all combined forcings) showed the gradual reduction in PR and most land surface variables until 1970 mainly because the AER effectively counteracted the GHG until 1970. Thereafter, balance was disturbed as AER began decreasing, producing a bouncing trend. The time-series plots of the historical simulation (integrated forcing) tended to lie between those of the GHG-only and AER-only simulation results, which indicates that the influences of GHG and AER are balanced. For instance, PR and ET showed balanced results between GHG and AER. However, the historical simulation of some other variables was skewed toward GHG or AER. For example, TAS and GPP in the historical simulations tended to follow the GHG-only results, while net radiation closely followed the AER-only results. The historical simulation of ET ratio and WUE also strongly followed the GHG trend. Each forcing impact and historical changes varied on a regional basis. Developing countries in the late 20 th century such as East Asia showed significant contrasts from other regions, possibly owing to the aerosol effect. For instance, the rebound of PR, TAS, and net radiation after 1970 was not observed in East Asia. Instead, further declines of these variables were noted, likely because Asian regions are still developing and suffering from air quality issues. Conversely, advanced countries such as North America and Europe showed an increasing trend in PR and net radiation. Moreover, distinct from the global analysis, the statistically significant MLRM parameters of AER (50%) outnumbered GHG and NAT in East Asia (Figure 9).