Multi-model ensemble of statistically downscaled GCMs over southeastern South America: historical evaluation and future projections of daily precipitation with focus on extremes

High-resolution rainfall information is of great value, particularly over southeastern South America (SESA) where the observed and projected climate changes pose a substantial threat to socio-economic activities hydrological sector. Consequently, this work focuses on construction an unprecedented multi-model ensemble statistically downscaled (ESD) global models (GCMs) for daily precipitation. Different statistical techniques were employed - including analogs, stochastic versions regression-based involving neural networks generalised linear regressions conditioned by weather types variety CMIP5 CMIP6 models. In general, most added value in representation main features precipitation, especially spatial intra-annual variability extremes. The sensible driving GCMs, although ESD family choice also introduced differences among simulations. projections depicted increases mean precipitation associated with rising frequency extreme events mostly during warm season following trends SESA. Change rates consistent up mid-21st century, when model spread started emerge. Furthermore, these compared available CORDEX-CORE RCM simulations, evidencing agreement between dynamical downscaling procedures terms sign changes, presenting their intensity. Overall, study evidences potential changing contributes its undergoing development