This study explores the potential of atmospheric moisture content, its transport and its divergence over the ocean and land as proxies for the variability of Indian summer monsoon rainfall (ISMR) for the period 1950–2019. The analyses using multiple linear regression reveal that the interannual and intraseasonal variability of ISMR and the mean ISMR is largely controlled by Arabian Sea moisture flux and Ganga river basin moisture content, and these parameters exhibit statistically significant high correlations in most regions. The regression model and the parameters are statistically significant and the model could explain rainfall variability of about 12%–50% in various regions. The model shows a false alarm rate (FAR) of 0.25–0.45 and a probability of detection (POD) of 0.43–0.50 for wet years in West Central, North West and North Central India. The FAR and POD are about 0.06–0.32 and 0.60–0.70, respectively for dry years in those regions. The model reproduces flood and drought years of about 32%–50% and 55%–70% in those regions. Also, the moisture indices could clearly identify the majority of wet and dry years that occurred during the period. The ISMR variability associated with moisture indices is unaffected by El Nio Southern Oscillation. Henceforth, this study demonstrates the significance of atmospheric moisture on regional rainfall distribution and suggests that these parameters can be used in both statistical and dynamical models to better predict monsoon and global precipitation.

这项研究探讨了大气水分含量，其在海洋和陆地上的运输及其分叉的潜力，以此作为1950-2019年印度夏季风降雨量（ISMR）变化的代理。使用多元线性回归进行的分析表明，ISMR的年际和季节内变化以及ISMR的平均值在很大程度上受阿拉伯海水分通量和恒河流域水分含量的控制，并且这些参数在大多数地区显示出统计学上显着的高度相关性。回归模型和参数具有统计学意义，该模型可以解释各个地区约12％–50％的降雨变化。该模型显示印度中西部，西北和中北部湿年的误报率（FAR）为0.25–0.45，检测概率（POD）为0.43–0.50。在这些地区，干旱年份的FAR和POD分别约为0.06-0.32和0.60-0.70。该模型再现了这些地区的洪水和干旱年分别约为32％–50％和55％–70％。同样，水分指数可以清楚地识别出这段时期内大部分的干湿年份。与湿度指数相关的ISMR变异性不受厄尔尼诺南方涛动的影响。此后，这项研究证明了大气水分对区域降雨分布的重要性，并暗示这些参数可用于统计和动力学模型，以更好地预测季风和全球降水。水分指数可以清楚地识别出这段时期内大部分的干湿年份。与湿度指数相关的ISMR变异性不受厄尔尼诺南方涛动的影响。此后，这项研究证明了大气水分对区域降雨分布的重要性，并暗示这些参数可用于统计和动力学模型，以更好地预测季风和全球降水。水分指数可以清楚地识别出这段时期内大部分的干湿年份。与湿度指数相关的ISMR变异性不受厄尔尼诺南方涛动的影响。此后，这项研究证明了大气水分对区域降雨分布的重要性，并暗示这些参数可用于统计和动力学模型，以更好地预测季风和全球降水。