Heteroscedastic Characteristics of Precipitation with Climate Changes in China

With global warming, previous studies have found nonuniformity responses of precipitation because of regional differences. However, climate change affects the mean, extreme, and data structure of precipitation. Quantile regression, which can reflect every part of the trends of data, was used to detect responses of each part of precipitation in China. The V2.0 dataset of daily precipitation grid data (0.5° × 0.5°) from 1961 to 2020 in China was used as practical observation data. Daily precipitation in 2015–2100 from the China Model BCC-CSM2-MR of scenarios SSP2-4.5 and SSP5-8.5 were chosen as future climate changes with moderate and high radiative forcing, respectively. On the basis of the sign consistency of the slope coefficients with quantile regression, the results of quantiles q = 0.3, 0.5, 0.7 and 0.9 were selected to represent low, median, high and flood precipitation, respectively. Precipitation in four seasons was separately analyzed to observe seasonal characteristics in China. For the observation data, precipitation had obviously different responses in the low and high percentiles and was present in mainly spring and summer. In spring, in the middle and lower Yangtze Plains, the low and median precipitation increased, whereas the high and flood precipitation significantly decreased. In summer, Heilongjiang Province and northern Inner Mongolia showed decreasing trends in the low quantile and increasing trends in the high quantile, indicating a completely opposite trend adjustment. These regions deserve more attention. However, obviously different responses in low and high percentiles were not so evident in future climate changes. Self-consistency in model data may weaken the heteroscedastic characteristics of precipitation.