Accurate precipitation estimates are quintessential for hydrologic modeling and climate studies. Different gridded precipitation products are available in any region, and selecting the best one is essential for hydroclimatic modeling and analysis. In the current study, observation- (APHRODITE), reanalysis- (IMDAA, ERA5-Land, PGF), satellite-based (IMERG, CHIRPS, PERSIANN-CDR), and hybrid (MSWEP) gridded precipitation products with different spatial and temporal resolutions are evaluated using several continuous, categorical, graphical, and interval-based performance measures towards detecting Indian Summer Monsoon Rainfall (ISMR) events and estimating their magnitudes for the subcontinent of India, considering IMD gauge-based gridded data as reference product. We confine our analysis to the monsoon season (i.e., June to September), the principal rainy season in the Indian sub-continent. The dearth of data and limited rain gauge-based observations from non-uniform sparse monitoring networks across India necessitated grid-to-grid comparative evaluations instead of point-to-grid assessments. We propose a new ranking framework to determine the suitability of precipitation datasets for twenty-seven hydroclimatic regions comprising homogeneous rainfall zones, Köppen-Geiger climate zones, and major river basins. Results from the comprehensive evaluation suggest that (1) APHRODITE, MSWEP, and ERA5-Land best approximate precipitation event occurrences across India, (2) MSWEP and ERA5-Land are most suitable (highest rank) alternatives at the regional level, while APHRODITE is found to be next suitable dataset owing to its persistent dry bias, (3) CHIRPS and IMERG have reasonably lower rank score across India, (4) close agreement of examined datasets is noted over semi-arid and sub-humid regions (e.g., peninsular and central India), whereas ERA5-Land, IMDAA, and APHRODITE fail to detect and reproduce the intensity of the events along the west coast and northeastern India, (5) PGF and PERSIANN-CDR are the least situated datasets. Moreover, the present study provides a unique and innovative perspective to characterise the precipitation over a vast topographic, ecologic, and climatic gradient region like India.

中文翻译：

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印度夏季风期间的降雨事件检测和量级估计：水文气候不同地区网格化降水数据集的综合评估


准确的降水估计是水文建模和气候研究的典型要素。任何地区都有不同的网格化降水产品，选择最好的产品对于水文气候建模和分析至关重要。在目前的研究中，使用几种连续的、分类的、图形的和基于间隔的性能措施来评估具有不同空间和时间分辨率的观测 （APHRODITE）、再分析 （IMDAA、ERA5-Land、PGF）、基于卫星的 （IMERG、CHIRPS、PERSIANN-CDR） 和混合 （MSWEP） 网格化降水产品，以检测印度夏季季风降雨 （ISMR） 事件并估计它们在印度次大陆的幅度，考虑到基于 IMD 仪表的网格数据作为参考产品。我们将分析局限于季风季节（即 6 月至 9 月），这是印度次大陆的主要雨季。由于缺乏数据，而且印度各地不均匀的稀疏监测网络基于雨量计的观测数据有限，因此需要进行电网到电网的比较评估，而不是点对点的评估。我们提出了一个新的排名框架，以确定 27 个水文气候区降水数据集的适用性，这些区域包括均匀降雨区、Köppen-Geiger 气候区和主要河流流域。 综合评估结果表明，（1） APHRODITE、MSWEP 和 ERA5-Land 是印度降水事件发生次数最接近的，（2） MSWEP 和 ERA5-Land 是区域层面最合适（最高等级）的替代方案，而 APHRODITE 由于其持续的干偏差而被发现是下一个合适的数据集，（3） CHIRPS 和 IMERG 在印度的排名得分相对较低， （4） 在半干旱和半湿润地区（例如，半岛和印度中部）观察到所检查的数据集非常一致，而 ERA5-Land、IMDAA 和 APHRODITE 无法检测和再现印度西海岸和东北部的事件强度，（5） PGF 和 PERSIANN-CDR 是位置最少的数据集。此外，本研究提供了一个独特和创新的观点来描述像印度这样广阔的地形、生态和气候梯度地区的降水。