Heavy rainfall events and associated floods have emerged as one of the great threats to society that mainly manifested due the climate change. The Indian summer monsoon (ISM) contributes 80 % of annual rainfall and is characterized mainly by high-intensity rainfall events (HiREs) in the recent era. We investigated the spatiotemporal variability of HiREs from a climate change perspective by accessing the India Meteorological Department's (IMD) observed daily gridded rainfall dataset (0.25° × 0.25°) from 1961 to 2020 during the ISM season. Our observational analysis shows that the ISM total and the frequency of low- to high-intensity rainfall events have significantly decreased mostly over the central northeastern, Jammu and Kashmir, and some places in the northeastern and central parts of India. However, they have significantly increased over Gujarat, the northwestern, the Western Ghats, and the southern parts of India during the present climate period (1991–2020) compared to the past climate period (1961–1990). Furthermore, we explored the fidelity of five Coordinated Regional Climate Downscaling Experiments (CORDEX) Regional Climate Models (RCMs) in simulating the spatiotemporal variability of ISM total rainfall and the frequency of low- to high-intensity rainfall events over India during the historical (1976–2005) and future periods (2006–2100). All CORDEX RCMs overestimate the ISM total rainfall over India's heavy rainfall zones during the historical period by ∼10–30 % compared to IMD observations. To improve CORDEX RCM's skills in simulating the frequency of low- to high-intensity rainfall events, we employed a percentile-based bias correction technique. Compared to non-bias-corrected outputs from the RCMs, the quantile-bias-corrected method significantly enhanced the probability of detection rate (hit rate) in all studied models for extreme, heavy, and moderate rainfall events, excluding light rainfall events. Interestingly, the improvement is greater for extreme events, followed by heavy and moderate rainfall events. The composite hit rate of all the models shows 381 %, 146 %, and 44 % improvement for extreme, heavy, and moderate events, respectively. It is noticed that the CCCMA model performed better than the other four CORDEX models in capturing the spatial patterns of ISM total rainfall and the frequency of total extreme and heavy rainfall events over higher rainfall zones in India. Additionally, this study suggests that there will likely be no significant changes in ISM total rainfall over India in the future, but the frequency of total extreme and heavy rainfall events will most likely increase, while the frequency of moderate rainfall events will likely decrease mostly over southern parts of India in future projections.

中文翻译：

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使用偏差校正的 CORDEX 模型预测印度低强度到高强度降雨事件的频率


强降雨事件和相关的洪水已成为主要由气候变化引起的对社会的巨大威胁之一。印度夏季风 （ISM） 贡献了 80% 的年降雨量，其主要特点是近期的高强度降雨事件 （HiRE）。我们通过访问印度气象局 （IMD） 从 1961 年到 2020 年 ISM 季节观测的每日网格降雨数据集 （0.25° × 0.25°），从气候变化的角度研究了 HiREs 的时空变化。我们的观测分析表明，ISM 总降雨量和低强度到高强度降雨事件的频率显着下降，主要集中在印度东北部中部、查谟和克什米尔以及印度东北部和中部的一些地方。然而，与过去的气候时期（1961-1990 年）相比，当前气候时期（1991-2020 年）古吉拉特邦、西北部、西高止山脉和印度南部的发病率显著增加。此外，我们探讨了五个协调区域气候降尺度实验 （CORDEX） 区域气候模型 （RCM） 在模拟历史 （1976-2005） 和未来 （2006-2100） 期间印度 ISM 总降雨量的时空变化和低强度到高强度降雨事件频率的保真度。与 IMD 观测相比，所有 CORDEX RCM 都高估了印度历史上强降雨区的 ISM 总降雨量 ∼10-30%。为了提高 CORDEX RCM 模拟低强度到高强度降雨事件频率的技能，我们采用了基于百分位数的偏差校正技术。 与 RCM 的非偏差校正输出相比，分位数偏差校正方法显著提高了所有研究模型中极端、大雨和中度降雨事件（不包括小雨事件）的检出率（命中率）的概率。有趣的是，极端事件的改善更大，其次是大雨和中雨事件。所有模型的综合命中率分别显示极端、重度和中度事件提高了 381%、146% 和 44%。值得注意的是，CCCMA 模型在捕捉印度 ISM 总降雨量的空间模式以及总极端和强降雨事件的频率方面表现优于其他四个 CORDEX 模型。此外，这项研究表明，未来印度的 ISM 总降雨量可能不会有重大变化，但极端和强降雨事件的频率很可能会增加，而在未来的预测中，中度降雨事件的频率可能会减少，主要是在印度南部地区。