Detecting distributional changes of annual rainfall indices in Taiwan using quantile regression

• Quantile regression-based distributional changes detection scheme is developed.
• Distributional changes for three rainfall indices in Taiwan are spatially diverse.
• Leftward and sharpened PDF is evident for annual rain days in Taiwan.

It is commonly recognized that hydrologic cycle has been intensified by climate change, which may lead to changes in mean, variability, and extremes of climate and hydrologic variables. This study aims to explore possible distributional changes of rainfall characteristics over time in Taiwan using quantile regression. A simplified nine-category distributional-change scheme, with focusing on changes of scale and location of empirical probability density function, is proposed in this study to examine distributional changes of rainfall characteristics. A total of 23 daily rainfall series in Taiwan over the period of 1947–2000 is selected for detecting distributional changes of annual rainfall, annual rain days, and annual 1-day maximum rainfall. Inconsistent variation patterns are observed since distributional changes of these three annual rainfall indices are respectively classified into 7, 6, and 7 categories. The prevalent distributional change is only noted for annual rain days because 14 out of 23 stations (60.9%) are classified as the Category VII (leftward and sharpened distribution). Considerable spatial diversity is also observed in Taiwan except that the distributional change of annual rain days classified as Category VII is clustered in North and South regions.