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Disasters caused by extreme precipitation under global warming are anticipated to have a strong negative impact on urban construction and social security. In this study, daily grid precipitation datasets of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) for the period 1961–2018 were extracted to explore the temporal and spatial characteristics of extreme precipitation by using regression analysis, moving average and kriging interpolation. The frequency and intensity indices showed an increasing trend, whereas a decreasing trend was found for the persistence indices, which indicates that GBA tends to slowly become wetter. The mean values of extreme precipitation indices (EPIs) in GBA generally increased from west to east and from north to south. Except for the indices of consecutive wet days and consecutive dry days, other EPIs showed an upward trend in most regions, especially in coastal cities where floods are more likely to occur. Principal component analysis and regression analysis showed that the correlations between the EPIs mostly passed the 0.05 significance test, which suggests that they had a good indicator of extreme precipitation in GBA. This study provides a theoretical basis for extreme precipitation disaster prevention and control within the urban agglomerations of the GBA.

Temporal and spatial variations of extreme precipitation in the Guangdong-Hong Kong-Macao Greater Bay area from 1961 to 2018