The proposition of a new algorithm facilitates the predictability of weak/strong monsoons that lead to drought/flood events, respectively, in the Indian summer monsoon rainfall (ISMR). The proposed method estimates skewed Gaussian kernel distribution in the extreme values extracted from the rainfall series, and confidence levels of drought and flood years are obtained using bootstrap. Using the selected Coupled Model Intercomparison Phase 5 (CMIP5) simulations under representative concentration pathways (RCP) 8.5 scenario, the proposed method detects that extreme droughts (at 99% confidence level) in India are likely to occur in 2024 and 2027 in the early 21st century. Similarly, models project that 2031, 2032, and 2033 will be the most prominent flood years. It is projected that the probability of drought occurrence is likely to increase by 16%. In contrast, it is expected to diminish flood events by 11% in the future under projected global warming. Notably, our analysis reveals that 23.4% of grids covering ~30% of the Indian region are likely to experience increased frequency and intensity of droughts during 2020–2029, mainly covering the Northeast, Central, and Southern India. Furthermore, during this period, the Northeast and some parts in the North would experience floods over 29.6% (which covers ~ 39%) of the total grids. The proposed algorithm may be used for drought and flood monitoring over any geographical terrain.

新算法的提出促进了分别在印度夏季风降雨（ISMR）中导致干旱/洪水事件的弱/强季风的可预测性。所提出的方法在从降雨序列提取的极值中估计偏高斯核分布，并使用自举法获得干旱和洪水年的置信度。使用在代表性集中路径（RCP）8.5情景下选择的耦合模型比较第5阶段（CMIP5）模拟，该建议方法检测到印度可能在2024年和2027年发生极端干旱（置信水平为99％）。世纪。同样，模型预测2031年，2032年和2033年将是最突出的洪水年份。预计干旱发生的可能性可能会增加16％。相反，在预计的全球变暖情况下，预计将来洪水事件将减少11％。值得注意的是，我们的分析表明，到2020年至2029年，覆盖印度地区约30％的23.4％的电网干旱频率和强度可能会增加，主要覆盖印度东北部，中部和南部。此外，在此期间，东北和北部的部分地区将遭受超过总网格的29.6％（约占39％）的洪灾。所提出的算法可以用于在任何地理地形上的干旱和洪水监测。2020年至2029年，覆盖印度地区约30％的4％的电网干旱频率和强度可能会增加，主要是印度东北部，中部和南部。此外，在此期间，东北和北部的部分地区将遭受超过总网格的29.6％（约占39％）的洪灾。所提出的算法可以用于在任何地理地形上的干旱和洪水监测。2020年至2029年，覆盖印度地区约30％的4％的电网干旱频率和强度可能会增加，主要是印度东北部，中部和南部。此外，在此期间，东北和北部的部分地区将遭受超过总网格的29.6％（约占39％）的洪灾。所提出的算法可以用于在任何地理地形上的干旱和洪水监测。