Multilevel factorial analysis for effects of SSPs and GCMs on regional climate change: a case study for the Yangtze River Basin

This study analyses future changes in temperature and precipitation over the Yangtze River Basin (YRB) throughout the twenty-first century by developing a regional factorial cluster analysis (RFCA) model on the basis of bias correction and spatial disaggregation (BCSD), cluster analysis and multilevel factorial analysis (MFA). In detail, BCSD is presented to downscale climate variables (i.e., daily mean temperature, maximum temperature, and total precipitation) of multiple global climate models (GCMs) under four shared socioeconomic pathways (SSPs). Evaluations shows BCSD can reasonably reproduce high spatial resolution climate predictions, especially temperature variables with great spatial correlation coefficients (i.e., larger than 0.9). Future climate changes are quantified by the Standard Euclidean Distance (SED), indicating climate change over YRB would spatially and seasonally undergo uneven distribution. Temperature changes have an overall south-to-north trend, with high variations in winter and summer. Precipitation changes over the upper reaches in winter and summer have larger variations than lower ones. Two hotspots are clustered with SED stabilities greater than 1.8, distributed in the Tibetan Plateau and the Yunnan–Guizhou Plateau, respectively. The effects of uncertainties (i.e., periods, GCMs and SSPs) and their interactions on predictions are analyzed through MFA. The individual effects of SSPs factor and its interactions with periods are worthy of consideration. The largest variation is observed under SSP585, with a total SED of 1.050; the smallest variation is observed under SSP370, with a total SED of 1.034. The interaction of SSPs and periods leads to a relatively stable interdecadal total SED under SSP126, which remains around 1.040.