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

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Abstract

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

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Acknowledgements

This work was supported by the University of Buenos Aires 2018-20020170100117BA, 20020170100357BA and the ANPCyT PICT-2018-02496 and PICT 2019–02933 projects. The authors acknowledge the WCRP CMIP5 and CMIP6 and CORDEX for producing and making available the model outputs used in this work.

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  1. Department of Atmospheric and Ocean Sciences, Faculty of Exact and Natural Sciences, University of Buenos Aires (DCAO-FCEN-UBA), Buenos Aires, Argentina

    Olmo ME, R Balmaceda-Huarte & ML Bettolli

  2. National Council of Scientific and Technical Research (CONICET), Buenos Aires, Argentina

    Olmo ME, R Balmaceda-Huarte & ML Bettolli

  3. Institut Franco-Argentin d’Estudes sur le Climat et ses Impacts, Unité Mixte Internationale (UMI-IFAECI/CNRS- CONICET-UBA-IRD), Buenos Aires, Argentina

    Olmo ME, R Balmaceda-Huarte & ML Bettolli

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ME, O., Balmaceda-Huarte, R. & Bettolli, M. Multi-model ensemble of statistically downscaled GCMs over southeastern South America: historical evaluation and future projections of daily precipitation with focus on extremes. Clim Dyn 59, 3051–3068 (2022). https://doi.org/10.1007/s00382-022-06236-x

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