Using quantile regression to detect relationships between large-scale predictors and local precipitation over northern China

Quantile regression (QR) is proposed to examine the relationships between large-scale atmospheric variables and all parts of the distribution of daily precipitation amount at Beijing Station from 1960 to 2008. QR is also applied to evaluate the relationship between large-scale predictors and extreme precipitation (90th quantile) at 238 stations in northern China. Finally, QR is used to fit observed daily precipitation amounts for wet days at four sample stations. Results show that meridional wind and specific humidity at both 850 hPa and 500 hPa (V850, SH850, V500, and SH500) strongly affect all parts of the Beijing precipitation distribution during the wet season (April-September). Meridional wind, zonal wind, and specific humidity at only 850 hPa (V850, U850, SH850) are significantly related to the precipitation distribution in the dry season (October-March). Impacts of these large-scale predictors on the daily precipitation amount with higher quantile become stronger, whereas their impact on light precipitation is negligible. In addition, SH850 has a strong relationship with wet-season extreme precipitation across the entire region, whereas the impacts of V850, V500, and SH500 are mainly in semi-arid and semi-humid areas. For the dry season, both SH850 and V850 are the major predictors of extreme precipitation in the entire region. Moreover, QR can satisfactorily simulate the daily precipitation amount at each station and for each season, if an optimum distribution family is selected. Therefore, QR is valuable for detecting the relationship between the large-scale predictors and the daily precipitation amount.