Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data is challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) estimates in the Yarlung Tsangpo–Brahmaputra River basin (YBRB) using two linear and two nonlinear methods, and their influence on extreme precipitation indices was assessed by cross-validation. Bias correction greatly improved the performance of extreme precipitation analysis. The ability of four methods to correct wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Local intensity scaling (LOCI) and quantile–quantile mapping (QM) performed better than linear scaling (LS) and power transformation (PT). This study would provide a reference for using gridded precipitation data in extreme precipitation analysis and selecting a bias-corrected method for rainfall products in data-sparse regions.

中文翻译：

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对基于量规的网格产品进行偏差校正，以改善雅鲁藏布江-布拉马普特拉河流域的极端降水分析

降雨数据的数量，质量，一致性，可用性和空间分布方面的关键差距限制了极端降水分析，并且网格化降水数据的应用由于其明显的偏差而具有挑战性。本研究使用两种线性和两种非线性方法纠正了雅鲁藏布-布拉马普特拉河流域（YBRB）中亚洲降水高度解析的观测数据集成，以评估水资源（APHRODITE）的估算，并通过交叉评估了它们对极端降水指数的影响。验证。偏差校正大大提高了极端降水分析的性能。四种方法校正湿天频率和变异系数的能力大不相同，从而导致极端降水指数存在很大差异。局部强度缩放（LOCI）和分位数-分位数映射（QM）的性能优于线性缩放（LS）和功率变换（PT）。该研究将为在极端降水分析中使用栅格化降水数据以及为数据稀疏地区的降雨产品选择偏差校正方法提供参考。