Multidimensional Evaluation of the Gridded Precipitation Datasets over the Source Region of the Yellow River

Abstract High-accuracy long-term precipitation data are essential to hydrometeorological and cryohydrological studies, especially in alpine regions where observations are sparse. However, existing precipitation datasets exhibit substantial discrepancies in magnitude and spatiotemporal patterns, posing a major challenge to water cycle research in these areas. This study first evaluated five datasets, including the China Meteorological Forcing Dataset (CMFD), its updated version (CMFDv2.0), the gridded daily observation dataset over China (CN05.1), the enhanced global dataset for the land component of the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5-Land), and the high-resolution near-surface meteorological forcing dataset for the Third Pole (TPMFD) region, against gauge observations in the source region of the Yellow River (SRYR), a typical alpine region. A new gridded precipitation dataset (named CCLI) with a spatial resolution of 0.1° × 0.1° and a time series spanning 1951–2020 were subsequently constructed based on the evaluations of five datasets and using the delta correction method. The CCLI and five other datasets were then evaluated locally by using gauge observations and regionally by considering the spatiotemporal consistency and uncertainty. The CCLI outperformed the other five datasets across monthly, seasonal, and annual scales at the local scale. It also showed strong spatiotemporal consistency with the other five datasets and the lowest uncertainty across multiple temporal scales, with grid uncertainty ranging from 0.66 to 7.89 mm month −1 . The CMFDv2.0 followed closely behind. In contrast, the ERA5-Land, CN05.1, and TPMFD datasets displayed considerable biases and high uncertainties. Further analysis based on the CCLI revealed a significant and consistent increase in precipitation of 2.03 mm yr −1 in the SRYR since the 1950s. This study provides a high-quality precipitation dataset and lays the groundwork for further exploration of water cycle studies. Significance Statement The main purpose of this study is to explore a more accurate precipitation dataset over the source region of the Yellow River (SRYR), as the accurate and long-term gridded precipitation data are critical for flood modeling, climate change impact assessment, and water resource management. Our results reveal that the newly long-term (1951–2020) developed precipitation dataset (CCLI) outperforms the five mainstream precipitation datasets [i.e., China Meteorological Forcing Dataset (CMFD), CMFDv2.0, the gridded daily observation dataset over China (CN05.1), the land component of the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5-Land), and Third Pole Meteorological Forcing Dataset (TPMFD)] in terms of both magnitudes and spatiotemporal patterns. In addition, precipitation from the CCLI demonstrates a significant increasing trend of 2.03 mm yr −1 since the 1950s. This study can provide critical data support for understanding alpine water cycles.