Simplifying Universal Thermal Climate Index Calculations with Symbolic Regression: Analyzing Extreme Heatwaves in China

This study develops and validates an interpretable symbolic regression surrogate for the Universal Thermal Climate Index (UTCI) in China’s temperate (Köppen-Geiger Cfa) region. From ERA5 June–August (JJA) data (2013 and 2021; tested on 2022), the search yielded a compact four-predictor linear expression in which air temperature, dewpoint temperature and clear-sky shortwave radiation (J m −2 , preceding-hour accumulation) raise UTCI, whereas wind speed reduces it. Dewpoint temperature captures the humidity that limits evaporative cooling under extreme heat. The surrogate achieved R 2 = 0.793 (RMSE ≈ 1.96°C), keeping most predictions in the correct heat-stress category. The linear form emerged from the symbolic-regression search rather than being predefined, whereas the same procedure recovered nonlinear expressions in other Köppen-Geiger climate zones, including arid (BWh) and subtropical-highland (Cwb) climates. Applied to daily HadGEM3-GC31-LL outputs, it projects a marked rise in mean JJA UTCI by late century, largest in the north, with variance also rising in the south. Under SSP5-8.5, domain-mean UTCI rises by 9.5°C above 1980–2000, exceeding 10°C in the north. Very-strong (UTCI > 38°C) days increase from near zero to approximately 39 per year and 99th-percentile exceedances from about 0.9 to around 76.5, significant under absolute and percentile thresholds. Being linear, the change (+1.19°C per °C warming) decomposes into temperature (86%), dewpoint (15%), radiation (2%) and wind (−3%), and diagnoses a small model bias (roughly +0.76°C) mainly from underestimated wind speed. These single-model results are a high-end estimate. Combining efficient computation with driver-level interpretability, this intuitive surrogate offers a new tool for climate projection and heat-risk assessment and adaptation planning.