Detection and attribution of trends of meteorological extremes in Central America

H. G. Hidalgo (), S. W. Chou-Chen, K. A. McKinnon, S. Pascale, D. Quesada-Chacón, E. J. Alfaro, P. Bautista-Solís, P. M. Pérez-Briceño, H. F. Diaz, T. Maldonado, E. R. Rivera and T. Nakaegawa
Additional contact information
H. G. Hidalgo: Universidad de Costa Rica
S. W. Chou-Chen: Universidad de Costa Rica
K. A. McKinnon: University of California
S. Pascale: University of Bologna
D. Quesada-Chacón: Potsdam Institute for Climate Impact Research
E. J. Alfaro: Universidad de Costa Rica
P. Bautista-Solís: Universidad Nacional, Centro Mesoamericano de Desarrollo Sostenible del Trópico Seco (Cemede-UNA)
P. M. Pérez-Briceño: Universidad de Costa Rica
H. F. Diaz: University of Hawaii at Manoa
T. Maldonado: Universidad de Costa Rica
E. R. Rivera: Universidad de Costa Rica
T. Nakaegawa: Meteorological Research Institute

Climatic Change, 2025, vol. 178, issue 5, No 9, 21 pages

Abstract: Abstract We present an analysis to determine whether historical trends in extreme precipitation and temperature indices, as well as in yearly averages of several climate variables can be associated in part with anthropogenic climate change or explained solely by natural causes. To achieve this, we use three methodologies: a) a climate model-based approach, b) a hybrid method that combines models and observations (1979–2019), and c) a climate observations-based method (1983–2016). For each methodology, we compare the climate change signal, represented by the historical trends, to the noise generated by simulated climate datasets (using models or statistical methods) that do not include human influence. Overall, the model-based method suggests possible detection of the human influence in most temperature extreme indices and in precipitation-related indices in the northern countries. The hybrid method detects human influence in significantly fewer variables, but in many cases, consistently with those of the model-based approach. Both the hybrid and observation-based methods exhibit similar noise variability to the model-based method. Notably, due to limitations in data availability, our analysis excludes the most recent five years, during which substantial warming and an increase of extreme events have been observed globally.

Keywords: Extreme events; Detection and attribution; Anthropogenic climate change; Central America (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10584-025-03940-5

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