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\begin{document}

\title{Comprehensive evaluations on the error characteristics of the
state-of-the-art gridded precipitation products over Jiangxi province in
2019}



\author[1]{Tulin Hong}%
\author[2]{Hongyi Li}%
\author[3]{Meiqiu Chen}%
\affil[1]{Land Consolidation and Rehabilitation Center of Jiangxi, Nanchang 330045, China}%
\affil[2]{Jiangxi University of Finance and Economics}%
\affil[3]{Jiangxi Agriculture University}%


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  \date{May 25, 2024}


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\begin{abstract}
Accurate knowledge of the precipitation estimates with high quality and
fine spatiotemporal resolutions is crucial to the precipitation science
communities. Reanalysis and satellite-based precipitation products are
two primary sources of precipitation estimates for various applications.
In this study, three latest reanalysis and satellite-based precipitation
products, namely ERA5 (released for public in 2018 and 2020), ERA5-Land
(released for public in 2019) and IMERG-Final (released for public as
V06B in 2019), are selected in order to figure out their error
characteristics against rain gauge observations at multiple scales over
Jiangxi province, south central China, in 2019. The main conclusions of
this study include but not limited to: (1) considering the accumulated
yearly precipitation amount, both reanalysis precipitation data and
satellite-based precipitation products have similar spatial patterns and
show overestimations; (2) except for the performance revealed by POD,
IMERG-Final generally outperforms ERA5 and ERA5-Land at multiple
temporal scales, especially in terms of CC, FAR and MFI; (3) ERA5 and
ERA5-Land precipitation products have similar spatiotemporal error
characteristics, and ERA5-Land, which has finer spatial resolution,
performs better than ERA5; (4) in the respect of the capabilities of
capturing precipitation events, the spatial characteristics of
IMERG-Final are the closest to those of rain gauges, while ERA5 and
ERA5-Land significantly overestimate the event duration and
underestimate the mean event precipitation rate. These findings could
help the state-of-the-art reanalysis and satellite-based precipitation
products improve the data quality in the future generations.%
\end{abstract}%



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