LAUSR

Abstract. Traditional statistic downscaling methods are processed on independent stations, which ignores spatial correlations and spatiotemporal heterogeneity. In this study, a spatiotemporally distributed downscaling model (STDDM) was developed. The method interpolated observations and GCMs (Global Climate Models) simulations to continual finer grids; then created relationship, respectively for each grid at each time. We applied the STDDM in precipitation downscaling of Poyang Lake Watershed using MRI-CGCM3 (Meteorological Research Institute Coupled Ocean-Atmosphere General Circulation Model3), with an acceptant uncertainty of ≤ 4.9 %, and created future precipitation changes from 1998 to 2100 (1998–2012 in the historical and 2013–2100 in RCP8.5 scenario). The precipitation changes showed increasing heterogeneities in temporal and spatial distribution under the future climate warming. In the temporal pattern, the wet season precipitation increased with change rate (CR) = 7.33 mm/10a (11.66 mm/K) while the dry season precipitations decreased with CR = −0.92 mm/10a (−4.31 mm/K). The extreme precipitation frequency and intensity were enhanced with CR = 0.49 days/10a and 7.2 mm•day -1 /10a respectively. In the spatial pattern, precipitations in wet or dry season showed an uneven change rate over the watershed, and the wet or dry area exhibited a wetter or drier condition in the wet or dry season. Analysis with temperature increases showed precipitation changes appeared significantly (p