This study presents a comprehensive evaluation of the mean and extreme surface air temperature and precipitation in the Coupled Model Intercomparison Project Phase 6 (CMIP6) multimodel ensemble simulations over the Tibetan Plateau. Simulations from 29 climate models are compared against gridded observations for the period 1961–2012. Results show that models reasonably reproduce the overall spatial patterns of the 1961–2012 averages of seasonal mean temperature and precipitation. Nevertheless, models tend to underestimate mean temperature and overestimate precipitation accumulations. To be specific, the multimodel mean estimates of seasonal average temperatures over the plateau are 0.5°C–2.6°C colder than observed, while the corresponding estimates of seasonal precipitation accumulations are 218% (spring: March–May), 76% (summer: June–August), 129% (autumn: September–November), and 533% (winter: December–February) of those observed. As in observations, models also well capture increasing trend of mean temperature and precipitation in all seasons but underestimate the rates of increasing trends for both variables in all seasons. Models’ ability to simulate temperature and precipitation extremes is also evaluated in terms of a set of chosen extreme indices defined by the Expert Team on Climate Change Detection and Indices. On average, models tend to underestimate the averages of annual maximum daily maximum temperature, annual minimum daily minimum temperature, frost days, warm nights, and consecutive dry days but overestimate the corresponding averages of annual maximum 5-day precipitation accumulations and simply daily intensity. Generally, models are able to simulate the signs of the trends in extreme indices but underestimate their magnitudes and misrepresent spatial patterns.

中文翻译：

---

青藏高原 CMIP6 模式中温度和降水模拟的评估

本研究对青藏高原耦合模式比对项目第 6 阶段 (CMIP6) 多模式集合模拟中的平均和极端地表气温和降水进行了综合评估。将 29 个气候模型的模拟结果与 1961-2012 年期间的网格观测值进行比较。结果表明，模型合理地再现了 1961-2012 年季节性平均气温和降水平均值的整体空间格局。然而，模型往往会低估平均温度并高估降水积累。具体而言，高原季节性平均气温的多模式平均估计值比观测值低 0.5°C–2.6°C，而季节性降水积累的相应估计值分别为 218%（春季：3-5 月）、76%（夏季：六月至八月），129%（秋季：9 月至 11 月）和 533%（冬季：12 月至 2 月）。与观察一样，模型也很好地捕捉了所有季节平均气温和降水的增加趋势，但低估了所有季节这两个变量的增加趋势。模型模拟温度和降水极端事件的能力也根据气候变化检测和指数专家组定义的一组选定的极端指数进行评估。平均而言，模型往往低估了年最高日最高气温、年最低日最低气温、霜日、暖夜和连续干旱日的平均值，而高估了相应的年最大 5 天降水积累和日强度的平均值。一般来说，