Our con dence in future climate projection depends on the ability of climate models to simulate the current climate, and model performance in simulating atmospheric circulation affects the ability to simulate extreme events. This study uses the self-organizing map (SOM) method to evaluate the frequency, persistence, and transition characteristics of models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for different ensembles of the 500 hPa daily geopotential height (Z500) in Asia, and then ranks all ensembles according to a comprehensive ranking metric (MR). Our results show that the SOM method is a powerful tool for assessing the daily-scale circulation simulation skills in Asia, and the results are not signi cantly affected by different map sizes. Positive associations between the performance of ensembles for any two of frequency, persistence, and transition were found, indicating that an ensemble that performs well for one metric is good for the others. The results of the MR ranking show that the r10i1p1f1 ensemble of CanESM5 gives the best overall simulation of 500 hPa circulation in Asia, and this is also the ensemble that best simulates frequency characteristics. The MR simulation skills of the 10 best ensembles for the position of the Western North Paci c Subtropical High (WNPSH) are far better than those of the 10 worst. Such differences may lead to errors in the simulation of extreme events. This study will help future studies in the choice of ensembles with higher circulation simulation skills to improve the credibility of their conclusions.

This paper uses monthly surface air temperature (SAT) data from the Climatic Research Unit dataset and the Coupled Model Intercomparison Project phase 6 multimodel ensemble simulations to investigate the variations in the annualmean and four seasons SAT across the Eurasian and North Africa landmasses and the possible causes. The SAT over multiple timescales has significant seasonal and regional differences. The trend, variance of decadal variation (DV), and variance of interannual variability (IV) show that the differences between Siberia and mid-low latitudes are much larger in winter and spring than in summer and autumn. The SAT exhibits a robust warming trend over Siberia in winter (0.21-0.42 CÁdecade −1 ) than over mid-low latitudes in summer (<0.07 CÁdecade −1 ). The cause is the combined diversity effects of greenhouse gases (GHG) and anthropogenic aerosols (AA) in four seasons over different regions. The warming trend of Siberia in winter is affected by the combination of strong warming effect by GHG and the weak cooling effect by AA, and the trend of mid-low latitudes in summer is opposite. The leading modes of the DV show a consistent spatial pattern over the whole Eurasia in four seasons while the IV show a meridional dipole pattern across 40 N except in summer.The internal climate variability is the main driver of the SAT IV and DV, but the effect of anthropogenic activity and natural forcing may reinforce the IV and DV in some regions. The combined role of GHG and AA forcings can significantly enlarge the DV in many parts of Eurasia, and the AA and natural forcing have significant effect on the IV in the high latitudes.

Our confidence in future climate projection depends on the ability of climate models to simulate the current climate, and model performance in simulating atmospheric circulation affects the ability to simulate extreme events. This study uses the self-organizing map (SOM) method to evaluate the frequency, persistence, and transition characteristics of models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for different ensembles of the 500 hPa daily geopotential height (Z500) in Asia, and then ranks all ensembles according to a comprehensive ranking metric (MR). Our results show that the SOM method is a powerful tool for assessing the daily-scale circulation simulation skills in Asia, and the results are not significantly affected by different map sizes. Positive associations between the performance of ensembles for any two of frequency, persistence, and transition were found, indicating that an ensemble that performs well for one metric is good for the others. The results of the MR ranking show that the r10i1p1f1 ensemble of CanESM5 gives the best overall simulation of 500 hPa circulation in Asia, and this is also the ensemble that best simulates frequency characteristics. The MR simulation skills of the 10 best ensembles for the position of the Western North Pacific Subtropical High (WNPSH) are far better than those of the 10 worst. Such differences may lead to errors in the simulation of extreme events. This study will help future studies in the choice of ensembles with higher circulation simulation skills to improve the credibility of their conclusions.

Soil moisture variations and its relevant feedbacks (e.g., soil moisture–temperature and soil moisture–precipitation) have a crucial impact on the climate system. This study uses reanalysis and Coupled Model Intercomparison Project phase 6 simulations datasets to detect, attribute, and project soil moisture variations. The effect of anthropogenic forcings [greenhouse gases (GHG), anthropogenic aerosols (AA), and land use (LU) change] on soil moisture is much larger than that of the natural forcing. Soil moisture shows a drying trend at a global scale, which is mainly attributed to GHG forcing. The effects of external forcings vary with the regions significantly. Over eastern South America, GHG, AA, and natural forcings make soil dry, while LU forcing makes the soil wet. Over severely drying Europe, all the external forcings including GHG, AA, LU, and natural forcing exhibit drying effect. The optimal fingerprint method detection results show that some of GHG, AA, LU, and natural signals can be detected in soil moisture variations in some regions such as Europe. The soil will keep drying in all scenarios over most parts of the globe except Sahel and parts of mid-latitudes of Asia. With the increase of anthropogenic emissions, the variation of global soil moisture will be more extreme, especially in hotspots where the land–atmosphere coupling is intensive. The drying trend of soil moisture will be much larger on the surface than in middle and deep layers in the future, and this phenomenon will be more severe under the high-emission scenario. It may be affected by increased evaporation and the effect of carbon dioxide fertilization caused by global warming.