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The rise in the Earth's global average surface temperature and the increase of extreme weather events have been the focus of scientific discussion in the last decades. Extreme heat combined with other environmental extremes, like high concentrations of air pollution, may induce health problems—especially in socioeconomically vulnerable populations. Spring in South America requires particular attention due to its association with hot, dry weather and air pollution in most parts of the continent. This paper intends to better comprehend the behaviour of heatwaves during the austral winter–spring transition. We propose identifying the spatial coverage, frequency and intensity of heatwaves in homogeneous areas of maximum air temperature near the surface (Tmax). We employed cluster analyses during the period between July and October for 41 years (1979–2019) through the ERA5 reanalysis. Homogeneous Tmax areas in South America were defined by cluster analyses that indicated three homogeneous Tmax regions, as follows: a larger area of the continent including the tropical region (Area 1), eastern and southeastern South America (Area 2) and southernmost and steep areas in the Andes (Area 3). The heatwave events identified via ERA5 reanalysis were classified according to their intensity (intense, moderate and weak events). Spatial frequency and trend analyses were also performed regarding the intensity and persistence of heatwave episodes. These methods allowed the identification of the behavioural aspect of heatwaves spanning the last four decades. The Mann–Kendall statistical test (MK) was applied in order to analyse the heatwave trend with a statistical significance level of 5%. A total of 191 heatwave episodes were identified. Of this total, 47.12, 35.60 and 17.28% of episodes occurred in Areas 2, 3 and 1, respectively. The hotter area extending from northeast to southwest in central South America stood out by its largest frequencies of intense heatwave episodes. Across the continent there was a significant increase in the intensity and persistence of heatwaves over the period of 1979 through to 2019. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the ability of a set of large ensembles (LE) of Global Climate Model (GCM) simulations from the Multi‐Model Large Ensemble Archive, Coupled Model Intercomparison Project Phase 6 (CMIP6) and database for Policy Decision making for Future climate change in reproducing the variability and change of the austral summer precipitation observed in South America along the second part of the 20th century and beginning of the 21st. LE show similar biases in the mean austral summer rainfall and interannual variability than those detected in previous model sets, such as the Coupled Model Intercomparison Project Phase 5 (CMIP5). The positive trends in south‐eastern South America (SESA) and negative ones in the southern Andes (SAn), that are the most significant observed regional features, are identified to some extent in LE simulations. The negative trend in SAn is a feature consistently shown among different models. For all models, mean trend is negative in that particular region and larger than the inter‐member dispersion for each large ensemble. Trend magnitudes in the SESA region show a larger dispersion between models than the SAn region. While the mean trend among models is consistent with the observations, models underestimate the observed trend. The multi‐model ensemble of selected models that best reproduce both mean and interannual variability features of rainfall in South America shows an 18% larger positive mean trend in SESA rainfall than those that have the worst performance. However, internal variability uncertainty is still higher than the mean trend for both sets of multi‐model ensembles [5% (18%) for selected (non‐selected) models]. Improvements in the representation of the main features of mean rainfall and variability are needed in order to better describe and reduce the uncertainties associated with the main climate change related signals in the continent. [ABSTRACT FROM AUTHOR]

The bias correction of the General Circulation Model (GCM) outputs has become a routine step that is taken in climate change impact assessments. To responsibly support the decision‐making processes, the climate‐modeling community has been debating about the conceptual requirements that bias‐correction methods should fulfill. Bearing in mind these requirements, we propose to decompose atmospheric variables into three temporal elements that represent the climate mean state, the interannual variability, and the daily variability. This decomposition is aimed at correcting the biases at one time scale without affecting the simulated climate (mean state) trend or the distributional properties at other time scales. The novelty of the proposed approach is, nevertheless, marked by the adjustment of interannual and daily variability that is made by replacing the GCM‐simulated data with synthetic samples drawn from Stable Distributions (SDs) that are fitted to the observed variability. The replacement prevents the transfer of the sampling variability of the calibration period and gives the corrected data the distributional properties of the observed climate. The employment of SDs was motivated by the fact that the climate‐change‐induced changes in the scale, the symmetry, and the frequency of extremes can be measured and applied to the SDs of the observed data. We correct the biases in the GCM‐simulated temperature and precipitation over northern South America using our proposed approach and two other existing ones. Our proposed method is capable of not only preserving the simulated climate trends but also reproducing the observed extremes as well as a more flexible method based on nonparametric distributions. Key Points: We decompose GCM's daily time series into terms that represent the climate mean state, the interannual variability, and the daily variabilityWe replace the modeled daily variability using trend‐preserving synthetic samples that are drawn from stable distributionsThe mathematical properties of stable distributions allow the inclusion of simulated climate change in the correction process [ABSTRACT FROM AUTHOR]