Rainfall is one of the most basic meteorological and hydrological elements. Quantitative rainfall estimation has always been a common concern in many fields of research and practice, such as meteorology, hydrology, and environment, as well as being one of the most important research hotspots in various fields nowadays. Due to the development of space observation technology and statistics, progress has been made in rainfall quantitative spatial estimation, which has continuously deepened our understanding of the water cycle across different space-time scales. In light of the information sources used in rainfall spatial estimation, this paper summarized the research progress in traditional spatial interpolation, remote sensing retrieval, atmospheric reanalysis rainfall, and multi-source rainfall merging since 2000. However, because of the extremely complex spatiotemporal variability and physical mechanism of rainfall, it is still quite challenging to obtain rainfall spatial distribution with high quality and resolution. Therefore, we present existing problems that require further exploration, including the improvement of interpolation and merging methods, the comprehensive evaluation of remote sensing, and the reanalysis of rainfall data and in-depth application of non-gauge based rainfall data.

Based on the long series of gauge rainfall data from 1979 to 2015, the performance of Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation dataset in the Zhoushan Archipelago and its surrounding sea area in Southeast China was evaluated from a variety of perspectives, and then the Cressman scheme was used to merge MSWEP with surface gauge measurements. It was found that at the spatial scale of 0.1° × 0.1°, MSWEP correctly detected most of the daily rainfall events in the study area. The surface precipitation was generally underestimated, with a relative deviation no more than 10%, but there was a fairly high miss reporting on heavy precipitation. The performance of MSWEP is also obviously characterized with seasonal fluctuation. Compared with the gauge records interpolation results, the accuracy statistics of rainfall dataset generated by merging MSWEP with gauge observations is improved to a certain degree. Especially its comprehensive identification ability of the dry and wet state for daily precipitation has been obviously raised. In addition, the merged data has the mixed characteristics of rain gauge observations and MSWEP in spatial structure. This paper has deepened the understanding of the performance of MSWEP in islands and sea areas, and also strengthened the understanding of the marginal effect of merging gauge data with MSWEP, even other global precipitation datasets.

The authors wish to make the following corrections to this paper