Wind-generated surface gravity waves forms an integral part in modulating the air-sea exchange processes. Information of wave parameters is also very essential in planning marine- and coastal-related activities. It is now well recognized that wind-wave activity shows changing trends over the global ocean basins. Numerous studies have addressed the projected changes in significant wave height for the Indian Ocean (IO) region, and there is a need to conduct thorough performance evaluation of global climate models (GCMs) over this region for futuristic planning. With this motivation, the present study examined the performance of historical dynamical wave climate simulations generated under the Coordinated Ocean Wave Climate Projections (COWCLIP) experiment. The simulations utilized near-surface wind speed datasets from 8 CMIP5 (Fifth phase of Coupled Model Intercomparison Project) GCMs to force a spectral wave model. The skill level of individual GCM forced wave simulations and multi-model mean (MMM) in reproducing the significant wave height (SWH) over four different sub-domains in the IO was evaluated with reference to the ECMWF Reanalysis 5th Generation (ERA5) datasets. Several performance metrics such as the Taylor Skill, M-Score, Model Climate Performance Index (MCPI), and Model Variability Index (MVI) are employed to establish the skill level of model simulations. The study deciphers that model performance is highly reliant on the region and its characteristics. Representation of the historical wave climate over the Arabian Sea (AS) and the Bay of Bengal (BoB) regions is remarkable in the COWCLIP datasets. However, there are discrepancies noticed in SWH distribution over the South Indian Ocean (SIO) attributed to model limitations in adequately reproducing swell wave fields over that region. The MMM constructed using the best-performing models (MRI-CGCM3, ACCESS1.0, INMCM4, HadGEM2-ES, and BCC-CSM1.1) is found consistent at all the sub-domains. The study signifies that the performance evaluation of GCM forced wave simulations is crucial before employing them for practical applications. Best-performing models listed from this study can be used to establish futuristic scenarios of SWH in a changing climate for the IO region.

风力产生的表面重力波形成了调节海气交换过程的组成部分。在计划与海洋和沿海相关的活动中，波浪参数的信息也非常重要。众所周知，风浪活动显示出全球海洋盆地的变化趋势。许多研究已经解决了印度洋（IO）地区重要波高的预计变化，因此有必要对该地区的全球气候模型（GCM）进行全面的性能评估，以进行未来规划。有了这种动机，本研究研究了在协调海浪气候预测（COWCLIP）实验下产生的历史动力海浪气候模拟的性能。该模拟利用了来自8个CMIP5（耦合模型比对项目的第五阶段）GCM的近地表风速数据集来强制建立谱波模型。参照ECMWF第5代重新分析（ERA5）数据集评估了单独的GCM强制波模拟和多模型均值（MMM）在IO中四个不同子域上再现有效波高（SWH）的技能水平。诸如泰勒技能，M得分，模型气候绩效指数（MCPI）和模型变异性指数（MVI）等几种绩效指标可用于建立模型仿真的技能水平。该研究认为，模型的性能高度依赖于该区域及其特征。在COWCLIP数据集中，阿拉伯海（AS）和孟加拉湾（BoB）地区的历史波浪气候的表示非常明显。但是，在南印度洋（SIO）上的SWH分布中发现了差异，这归因于该模型在充分再现该区域上涌浪场方面的模型局限性。发现使用最佳性能模型（MRI-CGCM3，ACCESS1.0，INMCM4，HadGEM2-ES和BCC-CSM1.1）构建的MMM在所有子域中都一致。该研究表明，在将GCM强迫波仿真用于实际应用之前，对其性能进行评估至关重要。本研究中列出的性能最佳的模型可用于建立IO地区气候变化的SWH的未来派情景。在南印度洋（SIO）上的SWH分布中发现了差异，这归因于该地区在充分再现该地区的涌浪场方面的模型局限性。发现使用最佳性能模型（MRI-CGCM3，ACCESS1.0，INMCM4，HadGEM2-ES和BCC-CSM1.1）构建的MMM在所有子域中都一致。该研究表明，在将GCM强迫波仿真用于实际应用之前，对其性能进行评估至关重要。本研究中列出的性能最佳的模型可用于建立IO地区气候变化的SWH的未来派情景。在南印度洋（SIO）上的SWH分布中发现了差异，这归因于该地区在充分再现该地区的涌浪场方面的模型局限性。发现使用最佳性能模型（MRI-CGCM3，ACCESS1.0，INMCM4，HadGEM2-ES和BCC-CSM1.1）构建的MMM在所有子域中都一致。该研究表明，在将GCM强迫波仿真用于实际应用之前，对其性能进行评估至关重要。本研究中列出的性能最佳的模型可用于建立IO地区气候变化的SWH的未来派情景。1）在所有子域中均一致。该研究表明，在将GCM强迫波仿真用于实际应用之前，对其性能进行评估至关重要。本研究中列出的性能最佳的模型可用于建立IO地区气候变化的SWH的未来派情景。1）在所有子域中均一致。该研究表明，在将GCM强迫波仿真用于实际应用之前，对其性能进行评估至关重要。本研究中列出的性能最佳的模型可用于建立IO地区气候变化的SWH的未来派情景。