Page 27 - APEC CLIMATE CENTER 2025 Annual Report

APEC CLIMATE CENTER 2025 ANNUAL REPORT

Highlighted

Achievements

in 2025

Fig 19 Diagnostic Characteristics of DCPP Models and Corresponding Customized Bias Correction
Strategies

This study scientifically identified the applicability and limitations of the DCPP system in
predicting extreme climate in the East Asia region. In particular, by revealing the different
Fig 17 Mean bias, decadal trend, temporal correlation and root mean squared error of detrended error characteristics and the degree to which each model reflects the warming trend, this
heat indices (TX90p and TXx) between ERA5 reanalysis data and DCPP hindcast model study provided essential foundational data for the future development of model-specific
data over East Asia for June-August. M1, M2, M3, M4 and M5 denote CanESM5, CMCC-
bias correction techniques. This is expected to fundamentally enhance the reliability of
CM2-SR5, HadGEM3-GC3.1-MM, IPSL-CM6A-LR and MPI-ESM1.2-HR, respectively. Yellow
stars are marked on the models with the lowest absolute bias, highest trend, highest climate prediction information and serve as a scientific foundation for more sophisticated
correlation and the lowest error. climate disaster response systems and the establishment of national climate policies.

Fig 18 Comparison of Multi-model Verification Scores (ROCS, BSS, and HSS) for East Asian
Drought Prediction across Lead-time Windows.

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