Long-term Analysis of Rainfall Variability and Gridded Precipitation Product Performance in Coastal Southeast China

Abstract. Accurate precipitation estimation is essential for hydrological applications and hazard monitoring in coastal regions characterized by complex terrain and strong land–sea interactions. This study analyzes long-term rainfall variability and evaluates the performance of six widely used gridded precipitation products, namely PERSIANN, IMERG, CHIRPS, ERA5-Land, GSMaP, and MSWEP, over the Guangdong–Hong Kong–Macao region during 2001–2023, using rain gauge observations as a reference. The results reveal pronounced spatial heterogeneity in precipitation trends, with coastal subregions showing a clear drying tendency, whereas the inland mountainous region remains relatively stable. Despite these differences, all regions exhibit highly synchronized interannual variability, indicating the dominant influence of large-scale climatic drivers. All products reproduce the unimodal seasonal cycle associated with the South China monsoon, with peak rainfall occurring in June. However, discrepancies increase during the peak rainy season, when intense convective precipitation enhances estimation uncertainty. Among the evaluated datasets, GSMaP and IMERG consistently outperform the others, with higher correlation coefficients and lower RMSE across most months, demonstrating strong capability in capturing complex precipitation dynamics. In contrast, PERSIANN shows substantial limitations, particularly during low-intensity rainfall periods, while ERA5-Land systematically underestimates peak rainfall intensity despite relatively stable performance. Product uncertainties also exhibit clear seasonal dependence, with the largest errors occurring during the pre-monsoon and monsoon periods. Overall, this study highlights the importance of long-term, region-specific evaluations of precipitation products in complex coastal environments and provides practical guidance for dataset selection in hydrological modeling and disaster risk management.