Applies wavelet bias correction to improve wind predictions in climate models, suggesting robust outcomes for wind-related applications.
Systematic biases in climate model outputs, particularly in extremes, limit their reliability for wind‐related applications. This study applies the Wavelet Bias Correction (WBC) method to daily near‐surface wind components (uwnd and vwnd) from three CMIP6 models (ACCESS‐CM2, HadGEM3‐GC31‐LL, and UKESM1‐0‐LL) over current (1979–2014) and future (2051–2086) periods, using National Center for Environmental Prediction reanalysis 2 reanalysis as a reference. The 1521‐grid study domain spans the western‐southern Pacific and Indian Oceans. Operating in the time‐frequency domain, WBC improves both magnitude and variability of simulated winds while explicitly preserving the physical co‐dependence of the u and v components, ensuring directional consistency. Percentile‐based evaluation shows that models generally underestimate median and extreme winds at the 99th percentile. Bias‐corrected projections indicate fewer extreme wind events (>49 km/hr) than in the current climate, consistent with observed declines in cyclone frequency. Results highlight WBC as a robust, physically consistent correction framework for wind simulations.
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Kusumastuti et al. (2026) studied this question.
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