Spatial correlations of daily precipitation over mainland China

Abstract

AbstractKnowledge of spatial correlations of precipitation is important for the generation of grid‐based surface precipitation data sets, deployment of data collection, selection of downscaling strategies, and interpretation of paleoclimate reconstructions. Spatial correlations of daily precipitation in China were analyzed based on a daily precipitation data set from 1951 through 2014 for 2,208 stations by dividing them into 13 regions. Interstation Pearson correlation coefficient r for the daily precipitation series and the corresponding interstation distance d were calculated for each region. The exponential spatial correlation model () was fitted by the r‐d pairs, in which c0, d0 and s0 were the parameter variance, scale and shape, respectively. The results showed that: (a) The determination coefficient R2 of the correlation model varied from 0.54 to 0.96, with a mean of 0.82 and the regional maximum correlation distance d0 varied from 102.2 to 201.7 km, with a mean of 155.2 km. Western regions generally had smaller d0 than eastern regions, which indicates rain events in the western regions were more local; (b) The goodness‐of‐fit of the model was improved by dividing samples into West‐East (W‐E) and North‐South (N‐S) directions. The average of d0 for all regions (190.2 km) for the W‐E direction is larger than that for the N‐S direction (142.9 km); (c) The correlation distances in summer and dry years are shorter than those in winter and wet years. However, the difference of correlation distance between dry and wet years was subtle compared with those between summer and winter, and between W‐E and N‐S directions. Seven regions were divided based on the spatial correlations of daily precipitation and different spatial models were suggested to be used for different regions, seasons and directions when the interpolation of daily precipitation is conducted for the generation of gridded surface precipitation data sets.