<p>Extreme precipitation is considered to be one of the natural disasters with greatest impact on human society, leading to floods and debris flows. To better understand the spatio-temporal effects on extreme precipitation, and to predict the intensity of extreme precipitation ahead in different return periods, this study focus on quantifying both climate and spatial effects on the intensity of extreme precipitation in coastal areas of southeast China, considering different weather system. A hierarchical Bayesian model with generalized extreme value distribution (GEV) is applied to maximum daily precipitation through 94 stations in study area from 1964 to 2013 in JAS. Tropical cyclone (TC) and non-TC influenced extreme precipitation are analyzed separately. Climate and spatial effects are introduced through regression models associating parameter values in GEV with different covariates, such as climate indices and distance to coastline. It was observed that SST anomaly in North Pacific, SLP anomaly above North India Ocean are found to be the main climate indices that influence extreme precipitation in coastal areas of southeast China. Using SST, we can predict the intensity of extreme precipitation in different return period at 6-month lag. Extreme precipitation was found to decrease as distance to coastline increase. In addition, different performances of extreme precipitation along with distance to coastline were found among various subregions and weather systems.</p>

This study investigated the spatial effects of the extreme precipitation during the raining season in the perspective of tropical cyclone‐related (TCEP) and non‐TC‐related extreme precipitation (nTCEP). The seasonal maximum 1‐day precipitation (RX1day) data across 94 stations in the coastal areas of southeastern China from 1964 to 2013 were used and partitioned into different homogenous regions. Regional Bayesian models were developed for generalized extreme value (GEV) distribution to quantify the spatial effects on extreme precipitation in each homogeneous region. Four coastal and one inland homogeneous regions were identified, which were consistent with the TC tracks. The extreme precipitation was found to decrease as the distance from the coastline increases, whose sensitivity to the distance from the coastline differs from region to region. For TCEP, the distance from the coastline has effects on the location and the scale parameters of the regional GEV model in all regions. The 50‐year return level of TCEP in southern regions is five times more sensitive to the distance from the coastline than that in the northern regions. For nTCEP, the distance from the coastline has effects on the scale parameter in all regions, while the location parameter is affected only in two regions in the south. The sensitivity of 50‐year return levels of nTCEP to the distance from the coastline decreases gradually from south to north. Within 200 km from the coastline, the 50‐year return level of TCEP is 15.6–78.9% larger than that of nTCEP in the same region.