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This article evaluates the performance of 20 Coupled Model Intercomparison Project phase 6 (CMIP6) models in simulating temperature and precipitation over China through comparisons with gridded observation data for the period of 1995–2014, with a focus on spatial patterns and interannual variability. The evaluations show that the CMIP6 models perform well in reproducing the climatological spatial distribution of temperature and precipitation, with better performance for temperature than for precipitation. Their interannual variability can also be reasonably captured by most models, however, poor performance is noted regarding the interannual variability of winter precipitation. Based on the comprehensive performance for the above two factors, the “highest-ranked” models are selected as an ensemble (BMME). The BMME outperforms the ensemble of all models (AMME) in simulating annual and winter temperature and precipitation, particularly for those subregions with complex terrain but it shows little improvement for summer temperature and precipitation. The AMME and BMME projections indicate annual increases for both temperature and precipitation across China by the end of the 21st century, with larger increases under the scenario of the Shared Socioeconomic Pathway 5/Representative Concentration Pathway 8.5 (SSP585) than under scenario of the Shared Socioeconomic Pathway 2/Representative Concentration Pathway 4.5 (SSP245). The greatest increases of annual temperature are projected for higher latitudes and higher elevations and the largest percentage-based increases in annual precipitation are projected to occur in northern and western China, especially under SSP585. However, the BMME, which generally performs better in these regions, projects lower changes in annual temperature and larger variations in annual precipitation when compared to the AMME projections.

Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during 1961–2005, the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), and 30 models from phase 5 of CMIP (CMIP5), are assessed in terms of spatial distribution and interannual variability. The CMIP6 multi-model ensemble mean (CMIP6-MME) can simulate well the spatial pattern of annual mean temperature, maximum daily maximum temperature, and minimum daily minimum temperature. However, CMIP6-MME has difficulties in reproducing cold nights and warm days, and has large cold biases over the Tibetan Plateau. Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices. Compared to CMIP5, CMIP6 models show improvements in the simulation of climate indices over China. This is particularly true for precipitation indices for both the climatological pattern and the interannual variation, except for the consecutive dry days. The areal-mean bias for total precipitation has been reduced from 127% (CMIP5-MME) to 79% (CMIP6-MME). The most striking feature is that the dry biases in southern China, very persistent and general in CMIP5-MME, are largely reduced in CMIP6-MME. Stronger ascent together with more abundant moisture can explain this reduction in dry biases. Wet biases for total precipitation, heavy precipitation, and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME, but smaller, compared to CMIP5-MME.

We compare the ability of coupled global climate models from the phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) in simulating the temperature and precipitation climatology and interannual variability over China for the period 1961\\2-2005 and the climatological East Asian monsoon for the period 1979–2005. All 92 models are able to simulate the geographical distribution of the above variables reasonably well. Compared with earlier CMIP5 models, current CMIP6 models have nationally weaker cold biases, a similar nationwide overestimation of precipitation and a weaker underestimation of the southeast\\3-northwest precipitation gradient, a comparable overestimation of the spatial variability of the interannual variability, and a similar underestimation of the strength of winter monsoon over northern Asia. Pairwise comparison indicates that models have improved from CMIP5 to CMIP6 for climatological temperature and precipitation and winter monsoon but display little improvement for the interannual temperature and precipitation variability and summer monsoon. The ability of models relates to their horizontal resolutions in certain aspects. Both the multi-model arithmetic mean and median display similar skills and outperform most of the individual models in all considered aspects.

Extreme high temperature (EHT) events are among the most impact-related consequences related to climate change, especially for China, a nation with a large population that is vulnerable to the climate warming. Based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), this study assesses future EHT changes across China at five specific global warming thresholds (1.5°C–5°C). The results indicate that global mean temperature will increase by 1.5°C/2°C before 2030/2050 relative to pre-industrial levels (1861–1900) under three future scenarios (SSP1-2.6, SSP2-4.5, and SSP5- 8.5), and warming will occur faster under SSP5-8.5 compared to SSP1-2.6 and SSP2-4.5. Under SSP5-8.5, global warming will eventually exceed 5°C by 2100, while under SSP1-2.6, it will stabilize around 2°C after 2050. In China, most of the areas where warming exceeds global average levels will be located in Tibet and northern China (Northwest China, North China and Northeast China), covering 50%–70% of the country. Furthermore, about 0.19–0.44 billion people (accounting for 16%–41% of the national population) will experience warming above the global average. Compared to present-day (1995–2014), the warmest day (TXx) will increase most notably in northern China, while the number of warm days (TX90p) and warm spell duration indicator (WSDI) will increase most profoundly in southern China. For example, relative to the present-day, TXx will increase by 1°C–5°C in northern China, and TX90p (WSDI) will increase by 25–150 (10–80) days in southern China at 1.5°C–5°C global warming. Compared to 2°C–5°C, limiting global warming to 1.5°C will help avoid about 36%–87% of the EHT increases in China.

This paper describes the datasets from the Scenario Model Intercomparison Project (ScenarioMIP) simulation experiments run with the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System Model, GridPoint version 3 (CAS FGOALS-g3). FGOALS-g3 is driven by eight shared socioeconomic pathways (SSPs) with different sets of future emission, concentration, and land-use scenarios. All Tier 1 and 2 experiments were carried out and were initialized using historical runs. A branch run method was used for the ensemble simulations. Model outputs were three-hourly, six-hourly, daily, and/or monthly mean values for the primary variables of the four component models. An evaluation and analysis of the simulations is also presented. The present results are expected to aid research into future climate change and socio-economic development.

Based on 20 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), this article explored possible reasons for differences in simulation biases and projected changes in precipitation in northern China among the all-model ensemble (AMME), “highest-ranked” model ensemble (BMME), and “lowest-ranked” model ensemble (WMME), from the perspective of atmospheric circulations and moisture budgets. The results show that the BMME and AMME reproduce the East Asian winter circulations better than the WMME. Compared with the AMME and WMME, the BMME reduces the overestimation of evaporation, thereby improving the simulation of winter precipitation. The three ensemble simulated biases for the East Asian summer circulations are generally similar, characterized by a stronger zonal pressure gradient between the mid-latitudes of the North Pacific and East Asia and a northward displacement of the East Asian westerly jet. However, the simulated vertical moisture advection is improved in the BMME, contributing to the slightly higher performance of the BMME than the AMME and WMME on summer precipitation in North and Northeast China. Compared to the AMME and WMME, the BMME projects larger increases in precipitation in northern China during both seasons by the end of the 21st century under the Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5). One of the reasons is that the increase in evaporation projected by the BMME is larger. The projection of a greater dynamic contribution by the BMME also plays a role. In addition, larger changes in the nonlinear components in the BMME projection contribute to a larger increase in winter precipitation in northern China.

As a member of the Chinese modeling groups, the coupled ocean-ice component of the Chinese Academy of Sciences’ Earth System Model, version 2.0 (CAS-ESM2.0), is taking part in the Ocean Model Intercomparison Project Phase 1 (OMIP1) experiment of phase 6 of the Coupled Model Intercomparison Project (CMIP6). The simulation was conducted, and monthly outputs have been published on the ESGF (Earth System Grid Federation) data server. In this paper, the experimental dataset is introduced, and the preliminary performances of the ocean model in simulating the global ocean temperature, salinity, sea surface temperature, sea surface salinity, sea surface height, sea ice, and Atlantic Meridional Overturning Circulation (AMOC) are evaluated. The results show that the model is at quasi-equilibrium during the integration of 372 years, and performances of the model are reasonable compared with observations. This dataset is ready to be downloaded and used by the community in related research, e.g., multi-ocean-sea-ice model performance evaluation and interannual variation in oceans driven by prescribed atmospheric forcing.

The outputs of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model for the decadal climate prediction project (DCPP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) are described in this paper. The FGOALS-f3-L was initialized through the upgraded, weakly coupled data assimilation scheme, referred to as EnOI-IAU, which assimilates observational anomalies of sea surface temperature (SST) and upper-level (0–1000-m) ocean temperature and salinity profiles into the coupled model. Then, nine ensemble members of 10-year hindcast/forecast experiments were conducted for each initial year over the period of 1960–2021, based on initial conditions produced by three initialization experiments. The hindcast and forecast experiments follow the experiment designs of the Component-A and Component-B of the DCPP, respectively. The decadal prediction output datasets contain a total of 44 monthly mean atmospheric and oceanic variables. The preliminary evaluation indicates that the hindcast experiments show significant predictive skill for the interannual variations of SST in the north Pacific and multi-year variations of SST in the subtropical Pacific and the southern Indian Ocean.

This study provides a comprehensive evaluation of historical surface soil moisture simulation (1979–2012) over Eurasia at annual and seasonal time scales between two medium-resolution versions of the Beijing Climate Center Climate System Model (BCC-CSM)—one that is currently participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6), i.e., BCC-CSM2-MR, and the other, BCC-CSM1.1m, which participated in CMIP5. We show that BCC-CSM2-MR is more skillful in reproducing the climate mean states and standard deviations of soil moisture, with pattern correlations increased and biases reduced significantly. BCC-CSM2-MR performs better in capturing the first two primary patterns of soil moisture anomalies, where the period of the corresponding time series is closer to that of reference data. Comparisons show that BCC-CSM2-MR performs at a high level among multiple models of CMIP6 in terms of centered pattern correlation and “amplitude of variation” (relative standard deviation). In general, the centered pattern correlation of BCC-CSM2-MR, ranging from 0.61 to 0.87, is higher than the multi-model mean of CMIP6, and the relative standard deviation is 0.75, which surmounts the overestimations in most of the CMIP6 models. Due to the vital role played by precipitation in land-atmosphere interaction, possible causes of the improvement of soil moisture simulation are further related to precipitation in BCC-CSM2-MR. The results indicate that a better description of the relationship between soil moisture and precipitation and a better reproduction of the climate mean precipitation by the model may result in the improved performance of soil moisture simulation.

The three-member historical simulations by the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System model, version f3-L (CAS FGOALS-f3-L), which is contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6), are described in this study. The details of the CAS FGOALS-f3-L model, experiment settings and output datasets are briefly introduced. The datasets include monthly and daily outputs from the atmospheric, oceanic, land and sea-ice component models of CAS FGOALS-f3-L, and all these data have been published online in the Earth System Grid Federation (ESGF, https://esgf-node.llnl.gov/projects/cmip6/ ). The three ensembles are initialized from the 600th, 650th and 700th model year of the preindustrial experiment (piControl) and forced by the same historical forcing provided by CMIP6 from 1850 to 2014. The performance of the coupled model is validated in comparison with some recent observed atmospheric and oceanic datasets. It is shown that CAS FGOALS-f3-L is able to reproduce the main features of the modern climate, including the climatology of air surface temperature and precipitation, the long-term changes in global mean surface air temperature, ocean heat content and sea surface steric height, and the horizontal and vertical distribution of temperature in the ocean and atmosphere. Meanwhile, like other state-of-the-art coupled GCMs, there are still some obvious biases in the historical simulations, which are also illustrated. This paper can help users to better understand the advantages and biases of the model and the datasets.