Spatial analysis of seasonal and annual heavy precipitation trends in Iran using quantile regression

The most significant factor that results in uncertainty about future water resource scarcity is climate change. Due to global warming, concerns about increasing or decreasing precipitation exist, which complicates water resource planning and management. Therefore, studying the trend of precipitation is of great importance. The linear trend reported in climate assessments reflects changes in average annual precipitation. However, the average trend cannot capture variations in other distribution aspects, including extreme precipitation events (high or low precipitation amounts). In this study, the Quantile Regression (QR) method was used to determine the trend of heavy precipitation (rainfall exceeding the 98th percentile) for seasonal and annual periods at 44 synoptic stations in Iran for two recent climatological standard normal periods: 1981-2010 and 1991-2020. For this purpose, after data quality control and homogenization, the 98th percentiles of seasonal and annual precipitation were calculated for both periods and compared. Then, the trends of these percentiles were estimated using the Quantile Regression method and tested. The results showed that compared to the two climatological standard normal periods, spring heavy precipitation had changed significantly in the southern of Alborz Mountain, summer heavy precipitation on the shores of the Caspian Sea, autumn heavy precipitation in the northwest and northeast regions of Iran, and winter heavy rainfall in the Zagros range. Similarly, spring-heavy precipitation in the southern of Alborz Mountain is decreasing, while summer-heavy precipitation on the shores of the Caspian Sea and autumn-heavy precipitation in the northwest and northeast regions of Iran are increasing.