With rising global temperatures, extreme weather events have become more frequent, intense and of longer duration. CMIP6 GCMs provide improved climate simulations that need robust evaluation for historical period for reliable future projections. The present study assesses the ability of bias corrected CMIP6 14 Global Climate Models (GCMs) in simulating heat wave over India for March–June during the historical period (1951–2014). Heat waves were identified using IITM criteria. Model biases were removed using variance scaling bias correction method that showed higher correlation (0.93) and lower root mean square error (2.15) and improvement in approximating the inter-annual variability as well as spatial patterns as observed maximum temperature after bias correction. Evaluation of model performance for 95th and 99th percentile maximum temperature and heatwaves showed that most of the models simulate these extremes similar to observation. Northwestern, Central and South-central regions recorded highest number of heatwaves with a frequency of 50 heatwave days/decade, which were captured by the most of the GCMs varying in decadal frequency over the region. Among the GCM, although all models were found competent, ACCESS-ESM1–5, MPI-ESM1–2HR and MRI-ESM2–0 models were found to be the best performing models for extreme indices and heat wave simulation over India. The study will aid to the current understanding of CMIP6-GCMs performances over the different meteorological subdivisions of India and pave way for future projection of heat waves as well as reduction in uncertainty among the models.

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偏差校正的 CMIP6 模型在印度不同气象分区的热浪模拟中的效果如何？

随着全球气温上升，极端天气事件变得更加频繁、剧烈和持续时间更长。 CMIP6 GCM 提供改进的气候模拟，需要对历史时期进行可靠的评估，以便对未来进行可靠的预测。本研究评估了偏差校正的 CMIP6 14 全球气候模型 (GCM) 模拟历史时期 (1951-2014) 3 月至 6 月印度热浪的能力。使用 IITM 标准来识别热浪。使用方差缩放偏差校正方法消除了模型偏差，该方法显示出更高的相关性（0.93）和更低的均方根误差（2.15），并且在近似年际变化以及偏差校正后观察到的最高温度的空间模式方面有所改进。对 95% 和 99% 最高温度和热浪的模型性能评估表明，大多数模型模拟了与观测类似的这些极端情况。西北、中部和中南部地区记录的热浪次数最多，热浪频率为 50 天/十年，该地区大多数不同年代际频率的 GCM 都捕获到了这些热浪。在 GCM 中，尽管所有模型都被认为是有能力的，但 ACCESS-ESM1-5、MPI-ESM1-2HR 和 MRI-ESM2-0 模型被认为是印度极端指数和热浪模拟中表现最好的模型。该研究将有助于当前了解 CMIP6-GCM 在印度不同气象分区的性能，并为未来热浪预测以及减少模型之间的不确定性铺平道路。