The current research aimed to evaluate the predictive skill of statistically downscaled National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) data in simulating the Indian summer monsoon rainfall (ISMR) for the period of 1961–2005 over the individual homogeneous monsoon regions of India (HMRI). For the purpose, five models are selected, as these models (in GCM) have shown better performance in the simulation of ISMR by the researcher. The spatial characteristics and statistical scores (annual cycle, percentage bias, Taylor score, probability distribution function) are used to evaluate the performance of each model in simulating rainfall over land points of individual HMRI. In the spatial analysis, it seems that models of NEX-GDDP can simulate the ISMR, pretty well in comparison to APHRODITE (observation), and show a moderate to significantly high correlation (grid point) over each of the HMRI particularly to core monsoon region, except over few parts of PI. The Taylor statistics suggest that the model CanESM performs very well over the regions of PI, NWI, and WCI. The models MPI-ESM-LR and NorESM perform well in simulating the ISMR over CNI, followed by ACCESS, CanESM, and CCSM4. The models have varying bias in predicting the rainfall; however, ACCESS does perform well and shows the minimum bias (ranges from ~ 1 to ~ 14% only) among others. The models CanESM and NorESM (except over CNI) performed relatively better. The NEX-GDDP models overcome the global climate models (GCMs) in the retrospective simulation of ISMR over the land points of India. It is concluded that the models have good predictability of JJAS rainfall but unable to catch daily rainfall variability.

当前的研究旨在评估统计缩减的国家航空航天局（NASA）地球交换全球每日缩减的投影（NEX-GDDP）数据的预测能力，以模拟1961-2005年印度夏季季风降雨（ISMR）。印度的单个均匀季风区（HMRI）。为此，选择了五个模型，因为这些模型（在GCM中）在研究人员对ISMR的仿真中表现出了更好的性能。空间特征和统计得分（年周期，偏差百分比，泰勒得分，概率分布函数）用于评估每个模型在模拟单个HMRI陆地上的降雨时的性能。在空间分析中，NEX-GDDP模型似乎可以模拟ISMR，与APHRODITE（观测）相比非常好，并且在每个HMRI（尤其是核心季风区域）上显示了中等到显着的相关（网格点），除了PI的几个部分。泰勒（Taylor）统计数据表明，CanESM模型在PI，NWI和WCI区域内表现良好。MPI-ESM-LR和NorESM模型在模拟CNI上的ISMR以及随后的ACCESS，CanESM和CCSM4方面表现良好。这些模型在预测降雨方面有不同的偏差。但是，ACCESS的性能确实很好，并且显示出最小的偏差（仅从〜1到〜14％的范围）。CanESM和NorESM（除CNI之外）模型的性能相对较好。NEX-GDDP模型在印度陆上ISMR的回顾性模拟中克服了​​全球气候模型（GCM）。