Drought events are critical environmental threats that yield several socioeconomic impacts. Such effects are even more relevant for South America (SA) since different activities essential for the continent, such as agriculture and energy generation, depend highly on water resources. Thus, this study aimed to evaluate future changes in precipitation and hydrological drought occurrence in SA through climate projections from eight global climate models (GCMs) of CMIP6. To this end, statistical downscaling was applied to the projections obtained using the quantile delta mapping technique, and the method proved to be efficient in reducing systematic biases and preserving GCMs’ trends. For the following decades, the results show considerable and statistically significant reductions in precipitation over most of SA, especially during the austral spring, with the most intense signal under the SSP5-8.5 forcing scenario. Furthermore, GCMs showed mixed signals about projections of the frequency and intensity of drought events. Still, they indicated agreement regarding the increased duration and severity of events over the continent and a substantial proportion of moderate and severe events over most of Brazil during the 21st century. These results can be helpful for better management of water resources by decision-makers and energy planners.

This study evaluated the performance of 50 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the statistical features of precipitation and air temperature in five subdomains of South America during the historical period (1995–2014). Monthly precipitation and temperature simulations were validated with data from the Climate Prediction Center Merged Analysis of Precipitation, the Global Precipitation Climatology Project, and the ERA5 reanalysis. The models’ performance was evaluated using a ranking analysis with statistical metrics such as mean, standard deviation, Pearson’s spatial correlation, annual cycle amplitude, and linear trend. The analyses considered the representation of precipitation and air temperature separately for each subdomain, the representation for all five regions together, and the joint representation of precipitation and air temperature for all five subdomains. In the Brazilian Amazon, the best-performing models were EC-Earth3-Veg, INM-CM4-8, and INMCM5-0 (precipitation), and IPSL-CM6A-LR, MPI-ESM2-0, and IITM-ESM (temperature). In the La Plata Basin, KACE-1-0-G, ACCESS-CM2, and IPSL-CM6A-LR (precipitation), and GFDL-ESM4, TaiESM1, and EC-Earth3-Veg (temperature) yielded the best simulations. In Northeast Brazil, SAM0-UNICON, CESM2, and MCM-UA-1-0 (precipitation), BCC-CSM2-MR, KACE-1-0-G, and CESM2 (temperature) showed the best results. In Argentine Patagonia, the GCMs ACCESS-CM2, ACCESS-ESM1-5 and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) outperformed. Finally, for Southeast Brazil, the models ACCESS-CM2, ACCESS-ESM1-5, and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) yielded the best simulations. The joint evaluation of the regions and variables indicated that the best models are CESM2, TaiESM1, CMCC-CM2-HR4, FIO-ESM-2-0, and MRI-ESM2-0.

A precipitação é uma variável de grande importância para a compreensão do clima em diferentes escalas espaciais como, por exemplo, a regional e global. Se os modelos climáticos conseguem reproduzir a variabilidade espacial e temporal da precipitação, se tem confiabilidade para o uso deles em estudos do clima futuro. Portanto, este estudo tem como objetivo avaliar a performance de 50 modelos climáticos globais do Coupled Model Intercomparison Project Phase 6 (CMIP6) a fim de selecionar aqueles que melhor simulam a climatologia da precipitação sobre dois subdomínios da América do Sul (sul da Amazônia – AMZ e sudeste do Brasil – SDE), no período histórico (1995-2014). Para isso, são realizadas análises estatísticas entre cada modelo e dados considerados como referência (Global Precipitation Climatology Project Version 1.2 - GPCP e Climate Prediction Center Merged Analysis of Precipitation - CMAP). Os resultados indicam que o modelo INM-CM5-0 é o que melhor simula a precipitação na AMZ e os modelos MPI-ESM1-2-HR e NESM3 na região SDE. Como estudos com modelos globais necessitam de muitos recursos computacionais, é mais conveniente selecionar aqueles que mostram boa performance para ambas as regiões, assim, são indicados os modelos EC-Earth3-Veg-LR, INM-CM4-8, INM-CM5-0 e MPI-ESM1-2-HR.Palavras-chave: Modelos de Circulação Geral, CMIP, IPCC, América do Sul.Performance of CMIP6 climate models in simulating precipitation in subdomains of South America in the historical period A B S T R A C TPrecipitation is a variable of great importance in understanding the climate at different spatial scales, such as regional and global. Whether climate models can reproduce the spatial and temporal variability of precipitation, they are reliable for their use in future climate studies. Therefore, this study aims to evaluate the performance of 50 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in order to select those that best simulate the climatology of precipitation over two subdomains of South America in the historical period (1995- 2014): southern Amazon (AMZ) and southeastern Brazil (SDE). In this sense, statistical analyses are performed between each model and data considered as reference (Global Precipitation Climatology Project Version 1.2, GPCP and Climate Prediction Center Merged Analysis of Precipitation - CMAP). The results indicate that the INM-CM5-0 model is the one with best performance in simulating precipitation in AMZ and MPI-ESM1-2-HR and NESM3 models in SDE. As studies with global models need a lot of computational resources, it is more convenient to select those models with good performance in both regions. In this way, EC-Earth3-Veg-LR, INM-CM4-8, INM-CM5-0 and MPI-ESM1-2-HR are the models indicated for both regions.Keywords: General Circulation Models, CMIP, IPCC South America

This study analyzed the main features (onset, demise, and length) of the South American Monsoon System (SAMS) projected in different time slices (2020–2039, 2040–2059, 2060–2079, and 2080–2099) and climate scenarios (SSP2–4.5 and SSP5–8.5). Eight global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that perform well in representing South America’s historical climate (1995–2014) were initially selected. Thus, the bias correction–statistical downscaling (BCSD) technique, using quantile delta mapping (QDM), was applied in each model to obtain higher-resolution projections than their original grid. The horizontal resolution adopted was 0.5° of latitude × longitude, the same as the Climate Prediction Center precipitation analysis used as a reference dataset in BCSD. The QDM technique improved the monsoon onset west of 60° W and the simulated demise and length in southwestern Amazonia. Raw and BCSD ensembles project an onset delay of approximately three pentads compared to the historical period over almost all regions and a demise delay of two pentads northward 20° S. Additionally, the BCSD ensemble projects a reduced length with statistical significance in most South Atlantic Convergence Zone regions and a delay of three pentads in the demise over the Brazilian Amazon from the second half of the 21st century.

Drought events are evident effects of climate change around the globe and yield several socio-economic impacts. Such effects are even more relevant for South America (SA) since different activities essential for the continent, like agriculture and energy generation, depend highly on water resources. Thus, this study aimed to evaluate future changes in precipitation and hydro-logical droughts occurrence in SA through climate projections from eight global climate models (GCMs) of CMIP6. To this end, statistical downscaling was applied to the projections with the Quantile Delta Mapping technique, and the method proved to be efficient in reducing systematic biases and preserving GCMs’ trends. For the following decades, the results show considerable and statistically significant reductions in precipitation over most of SA, especially during the austral spring, with the most intense signal under the SSP5-8.5 forcing scenario. Furthermore, GCMs showed mixed signals about projections of the frequency and intensity of drought events. Still, they indicated agreement regarding the increase in duration and severity of events over all of SA and a substantial proportion of moderate and severe events over most of Brazil during the 21st century. These results can be helpful for better management of water resources by deci-sion-makers and energy planners.

Little is known about how climate intervention through stratospheric aerosol injection (SAI) may affect the climatology of the Southern Hemisphere extratropical cyclones under warming scenarios. To address this knowledge gap, we tracked extratropical cyclones from 2015 to 2099 in a set of projections of three international projects: the Assessing Responses and Impacts of Solar Climate Intervention on the Earth System with Stratospheric Aerosol Injection (ARISE), the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS), and the Geoengineering Model Intercomparison Project (GeoMIP/G6sulfur). Comparisons were performed between no-SAI and SAI scenarios as well as between different timeslices and their reference period (2015–2024). Among the findings, both no-SAI and SAI project a decrease in cyclone frequency towards the end of the century although weaker under SAI scenarios. On the other hand, cyclones tend to be stronger under no-SAI scenarios while keeping their intensity more similar to the reference period under SAI scenarios. This means that under SAI scenarios the climatology of cyclones is less affected by global warming than under no-SAI. Other features of these systems, such as travelling distance, lifetime, and mean velocity show small differences between no-SAI and SAI scenarios and between reference and future periods.