DOI: 10.1017/eds.2026.10068 ISSN: 2634-4602

Evaluating bias-adjusted climate data for extreme heat metrics in agricultural applications

Muhammad Taky Tahmid
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Abstract

Bias adjustment is standard preprocessing for climate model output used in crop-impact and damage-assessment work. Most validation studies still emphasize mean temperature, even though agricultural applications often depend on nonlinear heat metrics built from daily extremes. We evaluate how bias adjustment propagates into Extreme Degree Day (EDD) metrics, which crop-yield and damage functions use to summarize heat stress above specified temperature cutoffs. Using ERA5 reanalysis as reference, we compare EDD from raw and bias-adjusted CMIP6 models (GFDL-ESM4, MPI-ESM1-2-HR, and EC-Earth3) across European ADM1 regions for 1994–2014. We compute EDD at seven thresholds (0, 4, 8, 12, 28, 30, and 32 °C), aggregate it with harvested-area weights for seven crops under rainfed and irrigated management, and sum it over crop-specific phenological windows. Bias adjustment reduces mean absolute error at low and moderate thresholds (EDD 

≤ $ \le $ less than or equals
 12 °C) by 37–86 °C
⋅ $ \cdot $ dot
days at EDD-0 and 29–64 °C
⋅ $ \cdot $ dot
days at EDD-8. At extreme thresholds (EDD 
≥ $ \ge $ greater than or equals
 28 °C),
Δ $ \Delta $ upper Delta
Skill is small on average (1–4 °C
⋅ $ \cdot $ dot
days at EDD-28), and bias adjustment increases error in about 40% of evaluated combinations. The split between helpful and unhelpful bias adjustment follows the EDD threshold more closely than model identity, which has implications for impact studies that treat bias-adjusted data as uniformly preferable.

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