The changing characteristics of precipitation extremes under global warming have recently received tremendous attention, yet the mechanisms are still insufficiently understood. The present study attempts to understand these processes over India by separating the ‘dynamic’ and ‘thermodynamic’ components of precipitation extremes using a suite of observed and reanalysis datasets. The former is mainly due to changes in atmospheric motion, while the latter is driven mainly by the changes associated with atmospheric moisture content. Limited studies have attributed dynamic and thermodynamic contributions to precipitation extremes, and their primary focus has been on the horizontal atmospheric motion component of the water budget. Our study, on the other hand, implements the decomposition of vertical atmospheric motion, based on the framework proposed by Oueslati et al. (Sci Rep 9: 2859, 2019), which has often been overlooked, especially for India. With the focus on two major and recent extreme events in the Kerala and Uttarakhand regions of India, we show that the vertical atmospheric motion has a more significant contribution to the events than the horizontal atmospheric motion. Further, decomposition of the vertical atmospheric motion shows that the dynamic component overwhelms the thermodynamic component’s contribution to these extreme events, which is found to be negligible. Using a threshold method to define extreme rainfall, we further extended our work to all India, and the results were consistent with those of the two considered events. Finally, we evaluate the contributions from the recently made available CMIP6 climate models, and the results are interestingly in alignment with the observations. The outcomes of this study will play a critical role in the proper prediction of rainfall extremes, whose value to climate adaptation can hardly be overemphasised.

全球变暖下极端降水的变化特征近年来受到了极大的关注，但其机制仍未得到充分理解。本研究试图通过使用一套观测和再分析数据集分离极端降水的“动态”和“热力学”成分来了解印度的这些过程。前者主要是由于大气运动的变化，而后者主要是由与大气水分含量相关的变化驱动的。有限的研究将动力和热力学贡献归因于极端降水，他们的主要重点是水收支的水平大气运动分量。另一方面，我们的研究实现了垂直大气运动的分解，基于 Oueslati 等人提出的框架。(Sci Rep 9: 2859, 2019)，这经常被忽视，尤其是在印度。通过关注印度喀拉拉邦和北阿坎德邦地区的两个主要和最近的极端事件，我们表明垂直大气运动对事件的贡献比水平大气运动更显着。此外，垂直大气运动的分解表明，动力分量压倒了热力学分量对这些极端事件的贡献，发现可以忽略不计。使用阈值方法来定义极端降雨，我们进一步将我们的工作扩展到整个印度，结果与所考虑的两个事件的结果一致。最后，我们评估了最近可用的 CMIP6 气候模型的贡献，有趣的是，结果与观察结果一致。这项研究的结果将在正确预测降雨极端事件中发挥关键作用，其对气候适应的价值再怎么强调也不为过。