The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community.

本研究评估了耦合模式比对项目（分别为 CMIP5 和 CMIP6）第 5 和第 6 阶段的全球气候模式 (GCM) 的相对性能，该模式基于它们模拟孟加拉国的年度和季节性平均降雨量和温度的能力。 1977-2005 年期间。使用多个统计指标来衡量 30 个气象观测站的 GCM 的性能。两种稳健的多标准决策分析方法用于整合使用不同指标获得的结果，以对 GCM 进行无偏排序。结果显示 MIROC5 是 CMIP5 GCM 中最熟练的，而 ACCESS-CM2 在 CMIP6 GCM 中是最熟练的。总体而言，与 CMIP5 MME 相比，CMIP6 MME 在模拟孟加拉国的降雨和温度方面表现出显着改善。在模拟高海拔地区的寒冷季节（冬季和季风后）降雨和温度方面发现了最大的改进。与气温相比，降雨的改善相对更多。除冬季降雨外，这些模型可以可靠地捕捉年度和季节性降雨和温度的年际变化。然而，孟加拉国的 CMIP6 模型中仍然存在系统性的湿偏和冷/暖偏差。CMIP6 GCM 与观测数据显示出更高的空间相关性，但与 CMIP5 GCM 相比，标准偏差和中心均方根误差的差异更大表明在模拟地理分布方面的性能更好，但在模拟除最小值外的大多数气候变量的空间变异方面的性能较低不同时间段的温度。在泰勒技能得分方面，CMIP6 MME 在模拟降雨方面表现出更高的性能，但在大多数时间范围内比 CMIP5 MME 在模拟温度方面表现出较低的性能。本研究的结果表明，CMIP6 模型中降雨和温度模拟的附加值在本研究中使用的气候模型之间并不一致。然而，它为科学界未来的气候变化风险评估研究开创了先例。