What question did this study set out to answer?

Evaluate the capacity of 25 CMIP6 general circulation models to simulate extreme precipitation indices during wet and dry seasons across complex regional topography from 1981 to 2014.

August 20, 2026ClimateOpen Access

Performance of CMIP6 GCMs in Representing Extreme Precipitation in Peru (1981–2014)

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Authors

GCGustavo De la Cruz ECEduardo Chávarri-VelardeUniversidad Nacional Agraria La Molina WLWaldo Lavado‐CasimiroServicio Nacional de Meteorología e Hidrología del Perú

Discussion

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Overview

Model evaluation reveals variable accuracy and persistent drizzle bias across CMIP6 simulations of extreme precipitation, indicating the necessity of regional downscaling and bias correction.

Key Points

Evaluate the capacity of 25 CMIP6 general circulation models to simulate extreme precipitation indices during wet and dry seasons across complex regional topography from 1981 to 2014.
Compared 25 CMIP6 model simulations against observational gridded precipitation data from the PISCO dataset for the period 1981–2014.
Calculated multiple extreme precipitation indices (including Rx1day, Rx5day, SDII, CDD, CWD, R10mm, and PRCPTOT) across wet and dry seasons.
Evaluated model skill using percent bias (PBIAS), normalized root-mean-square error (NRMSE), and pattern correlation coefficient (PCC), synthesising results via TOPSIS multi-criteria ranking.
NorESM2-MM, MPI-ESM1-2-LR, and CESM2 achieved the highest performance rankings (TOPSIS > 0.8; PCC frequently > 0.8) with relatively low biases, whereas FGOALS-g3 and CanESM5 performed poorly (TOPSIS < 0.5; PBIAS > 80% for Rx1day and Rx5day).
Most models exhibited a persistent drizzle bias, overestimating wet day frequency by 20–40% in the wet season and 40–80% in the dry season.
Temporal persistence metrics posed significant challenges across the ensemble, with consecutive wet days (CWD) frequently overestimated by 100–200%.

Cite This Study

Cruz et al. (2026) studied this question.

synapsesocial.com/papers/6a86b5eb8a91293e6a1cd90b https://doi.org/10.3390/cli14080165

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August 20, 2026ClimateOpen Access

Performance of CMIP6 GCMs in Representing Extreme Precipitation in Peru (1981–2014)

View Full Paper

Ask AI

Bookmark

Authors

GCGustavo De la Cruz ECEduardo Chávarri-VelardeUniversidad Nacional Agraria La Molina WLWaldo Lavado‐CasimiroServicio Nacional de Meteorología e Hidrología del Perú

Discussion

Member takes

Overview

Model evaluation reveals variable accuracy and persistent drizzle bias across CMIP6 simulations of extreme precipitation, indicating the necessity of regional downscaling and bias correction.

Key Points

Evaluate the capacity of 25 CMIP6 general circulation models to simulate extreme precipitation indices during wet and dry seasons across complex regional topography from 1981 to 2014.
Compared 25 CMIP6 model simulations against observational gridded precipitation data from the PISCO dataset for the period 1981–2014.
Calculated multiple extreme precipitation indices (including Rx1day, Rx5day, SDII, CDD, CWD, R10mm, and PRCPTOT) across wet and dry seasons.
Evaluated model skill using percent bias (PBIAS), normalized root-mean-square error (NRMSE), and pattern correlation coefficient (PCC), synthesising results via TOPSIS multi-criteria ranking.
NorESM2-MM, MPI-ESM1-2-LR, and CESM2 achieved the highest performance rankings (TOPSIS > 0.8; PCC frequently > 0.8) with relatively low biases, whereas FGOALS-g3 and CanESM5 performed poorly (TOPSIS < 0.5; PBIAS > 80% for Rx1day and Rx5day).
Most models exhibited a persistent drizzle bias, overestimating wet day frequency by 20–40% in the wet season and 40–80% in the dry season.
Temporal persistence metrics posed significant challenges across the ensemble, with consecutive wet days (CWD) frequently overestimated by 100–200%.

Cite This Study

Cruz et al. (2026) studied this question.

synapsesocial.com/papers/6a86b5eb8a91293e6a1cd90b https://doi.org/10.3390/cli14080165

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