Intercomparison of multiple high-resolution precipitation products over China: Climatology and extremes

An accurate and high spatiotemporal resolution precipitation observation dataset is crucial to the studies of climate change and related impact assessments. Great efforts have been made to evaluate the performance of a variety of precipitation products. However, most studies mainly focused on the assessment of precipitation estimation or precipitation occurrence detection, and seldom evaluated the capabilities of monitoring extreme precipitation of the utilized datasets. We conduct a study to quantitatively evaluate eight high-resolution precipitation products, including APHRODITE, CHIRPS, CMFD, CMORPH, IMERG, MSWEP, PERSIANN, and TMPA, against dense rain gauges in terms of precipitation climatology and extremes over China from 2001 to 2015. Results indicate that CMFD is superior to the rest products in terms of estimating precipitation amounts and monitoring precipitation extremes in China. APHRODITE performs best among all products in detecting precipitation occurrences. Precipitation products also exhibit different levels of performance in four subregions with distinct regional features. For both precipitation climatology and extremes, CMFD has the best applicability in the humid region and the Tibetan Plateau, while APHRODITE and MSWEP outperform other products in the wetto-arid transition and arid regions, respectively. This work could provide a valuable basis for bias correction and downscaling of global climate model outputs. It also has implications for the selection of precipitation products for local studies and the future development of precipitation products in China.

Automated summary

This study evaluates the performance of eight high-resolution precipitation products in China and finds that CMFD performs best in estimating precipitation amounts and monitoring extreme precipitation, while APHRODITE is the best at detecting precipitation occurrences. The performance of different products also varies in different regions.