The present study evaluates the performance of Conformal-Cubic Atmospheric Model (CCAM) simulations downscaled from six global climate models (GCMs) (i.e., ACCESS1.0, CNRM-CM5, CCSM4, GFDL-CM3, MPI-ESM-LR, and NorESM-M) and Max Plank’s Regional Model (REMO2009(MPI)) obtained from the South-Asia Coordinated Regional Climate Downscaling Experiment (CORDEX) for analyzing the summer monsoon maximum temperature (T_max) over the fifteen Agro-Climatic Zones (ACZs) in India. The model simulations are compared with the two sets of observed data obtained from the India Meteorology Department (IMD) and Climate Research Unit (CRU) for the period from 1981 to 2005. The results illustrate that the skill of CCAM regional climate models (RCMs) is higher than the REMO in simulating the T_max over all the regions. The spatial patterns of T_max in CCAM (CCSM) and CCAM (CNRM) are closer to IMD, while the T_max distributions in CCAM (CNRM), CCAM (CCSM), and CCAM (BCCR) agree well with the CRU, and correlation coefficient (CC) is more than 0.6; however, large positive biases in all RCMs are depicted over the Himalayan regions. The inter-comparison among all the RCMs suggest that the CCAM (CNRM) and CCAM (CCSM) are rendering as the foremost models in simulating T_max over different ACZs. Performances of these two models also infer the usefulness of the model products for impact studies over the individual ACZs. However, the existing systematic biases in the RCMs impeded the model performance and it is necessary to remove the model bias prior to some real-time application. In this study, two bias correction methods, i.e., linear scaling (LS) and distribution mapping (DM), have been used to correct RCM output bias. It is found that the model performance using DM correction is better than LS method. The performance validations are evaluated based on the probability density function (PDF), CC, and standard deviation (SD) with 95% confidence level. The model evaluation has also been justified using mean absolute error (MAE) index, Nash-Sutcliffe coefficient (NS) index, percent bias (P_bias), and the Willmott’s index of agreement (d) which confirm the research findings. The results are providing an effective guidance on the usefulness of bias corrected RCMs over a particular ACZs for impact assessment.

本研究评估了从六个全球气候模型（GCC）（即ACCESS1.0，CNRM-CM5，CCSM4，GFDL-CM3，MPI-ESM-LR和NorESM）缩减规模后的共形立方大气模型（CCAM）模拟的性能-M）和Max Plank的区域模型（REMO2009（MPI）），该模型是通过南亚协调区域气候降尺度实验（CORDEX）获得的，用于分析15个农业气候区（ACZ）的夏季季风最高温度（T _max）印度。将模型模拟与从印度气象局（IMD）和气候研究部门（CRU）获得的1981年至2005年的两组观测数据进行了比较。结果表明，CCAM区域气候模型（RCM）的技能在模拟T时比REMO高所有区域的_最大值。的空间图案Ť_最大值在CCAM（CCSM）和CCAM（CNRM）更接近IMD，而Ť_最大值在CCAM分布（CNRM），CCAM（CCSM），和CCAM（BCCR）与CRU吻合，并且相关系数（CC）大于0.6；然而，在喜马拉雅地区描绘了所有RCM中的大正偏差。所有RCM之间的相互比较表明，在模拟T _max时，CCAM（CNRM）和CCAM（CCSM）被渲染为最主要的模型。在不同的ACZ上。这两个模型的性能还可以推断出模型产品对单个ACZ的影响研究的有用性。但是，RCM中现有的系统偏差妨碍了模型性能，因此有必要在进行实时应用之前消除模型偏差。在这项研究中，两种偏差校正方法，即线性缩放（LS）和分布映射（DM），已用于校正RCM输出偏差。发现使用DM校正的模型性能优于LS方法。基于具有95％置信度的概率密度函数（PDF），CC和标准偏差（SD）评估性能验证。还使用平均绝对误差（MAE）指数，纳什-苏克利夫系数（NS）指数，偏差百分比（P_偏差）证明了模型评估的合理性。），以及确认研究结果的Willmott同意指数（d）。该结果为针对特定ACZ进行偏倚校正的RCM的有效性评估提供了有效的指导。