Editing IPCC:AR6/WGI/Chapter-Atlas (section)

==== Atlas.5.1.3 Assessment of Model Performance ====

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Current climate models perform poorly insimulating the mean precipitation in East Asia, including the phase of the northward progression of the seasonal rainband (M. [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ). Although there has been an improvement in the simulation of mean states, interannual variability and past climate changes in the progression from CMIP3 to CMIP5, some previously documented biases (such as the ridge position of the western North Pacific Subtropical High and the associated rainfall bias) are still evident in CMIP5 models ( [[#Sperber--2013|Sperber et al., 2013]] ; [[#Zhou--2017|Zhou et al., 2017]] ). Most models capture the main characteristics of the winter mean circulation over East Asia reasonably well, but they still suffer from difficulty in predicting the interannual variability of the EAWM ( [[#Shin--2018|Shin and Moon, 2018]] ). Models have improved from CMIP5 to CMIP6 for climatological temperature and EAWM (D. [[#Jiang--2020|]] [[#Jiang--2020|Jiang et al., 2020]] ). Some CMIP6 models also show improvements in simulating the annual mean and interannual variation of precipitation ( [[#Sellar--2019|Sellar et al., 2019]] ; [[#Tatebe--2019|Tatebe et al., 2019]] ; T. [[#Wu--2019|]] [[#Wu--2019|Wu et al., 2019]] ). The performance of models is sensitive to cumulus convection schemes and horizontal resolution ( [[#Haarsma--2016|Haarsma et al., 2016]] ; [[#Wu--2017|Wu et al., 2017]] ; [[#Kusunoki--2018b|Kusunoki, 2018b]] ). High-resolution atmospheric global climate models (AGCM) successfully reproduce the intensity and the spatial pattern of the EASM rainfall ( [[#Li--2015|Li et al., 2015]] ; [[#Yao--2017|Yao et al., 2017]] ; [[#Ito--2020a|Ito et al., 2020a]] ) and improve the simulation of the diurnal cycle of precipitation rates and the probability density distributions of daily precipitation over Korea, Japan and northern China ( [[#Lin--2019|Lin et al., 2019]] ), but increasing horizontal resolution (at the typical scales used in GCMs) is not always a panacea for solving model biases ( [[#Roberts--2018|Roberts et al., 2018]] ).

Recent studies using CORDEX-EA models with resolution of about 12–25 km showed that the RCMs produce relatively more detailed regional features of the temperature distribution compared with the driving GCMs ( [[#Tang--2016|Tang et al., 2016]] ). Over China, RCMs provide more spatial details and in general reduce the biases of their driving GCMs, in particular in DJF (December–January–February) and over areas with complex topography ( [[#Wu--2020|Wu and Gao, 2020]] ). However, RCMs also show biases in simulating East Asian precipitation and its variability ( [[#Park--2016|Park et al., 2016]] ; [[#Zhou--2016|Zhou et al., 2016]] ; [[#Zou--2016|Zou and Zhou, 2016]] ), and do not always show added value compared to the driving GCMs ( [[#Li--2018b|Li et al., 2018b]] ). For example, by comparing inter-GCM and inter-RCM differences around the Japan archipelago, it was found that RCM generate relatively large differences in precipitation ( [[#Suzuki-Parker--2018|Suzuki-Parker et al., 2018]] ). The RCM multi-model ensemble produces superior simulation compared to that of a single model ( [[#Jin--2016|Jin et al., 2016]] ; D.-L. [[#Guo--2018|]] [[#Guo--2018|Guo et al., 2018]] ). A comparative study of RCMs at different spatial resolutions showed that with coarse resolution they present some limitations and high-resolution RCMs offer added value for several evaluation metrics ( [[#Park--2020|Park et al., 2020]] ).

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