{'fraction_non_alphanumeric': 0.024152542372881357, 'fraction_numerical': 0.03347457627118644, 'mean_word_length': 5.164490861618799, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

General circulation models (GCMs) are indispensable for climate change adaptive study over the Tibetan Plateau (TP), which is the potential trigger and amplifier in global climate fluctuations. With the release of Coupled Model Intercomparison Project Phase 6 (CMIP6), 24 GCMs from CMIP5 and CMIP6 were comparatively evaluated for precipitation and air temperature simulations based on the China Meteorological Forcing Dataset (CMFD). Rank score results showed that CMIP6 models generally performed better than CMIP5 for precipitation and surface air temperature over the TP. According to multimodel ensembles (MMEs) of the optimal GCMs for each climate variable, the overestimation of precipitation was both present in CMIP5 and CMIP6, but the results of CMIP6 MMEs were relatively lower in the mid‐west and northern edge of the TP. Furthermore, CMIP6 offered a better performance of precipitation in summer and autumn. For temperature, CMIP6 MMEs were able to reduce the relatively large cold bias that appeared in CMIP5 MMEs in northwest areas to about 1°C and had a smaller bias in spring and winter. Moreover, the investigation into the simulation effects of precipitation at different elevation zones demonstrated that the improved ability of CMIP6 MMEs to reduce bias was mainly concentrated in the elevation zones of 2,000–3,000 m and over 5,000 m, where the precipitation bias was more than 200%. Additionally, CMIP6 MMEs of temperature were able to reduce the bias to less than 2°C in each elevation zone, with the minimum bias of −0.22°C distributed in the region with altitudes from 3,000 to 4,000 m, while the biases of CMIP5 MMEs in the region of 4,000–5,000 m and over 5,000 m were smaller than those of CMIP6 MMEs. Findings obtained in this study could provide a scientific reference for related climate change research over the TP. GCMs of CMIP6 perform better than those of CMIP5 for precipitation and temperature over the TP. Multimodel ensembles (MMEs) of CMIP6 effectively reduce the overestimation of precipitation from CMIP5 MMEs by 40 mm at the annual scale. Improved ability of CMIP6 MMEs shows a significant elevation dependency, especially in elevation zones of 2,000–3,000 m and over 5,000 m for precipitation.