Substantial increases in future precipitation extremes – insights from a large ensemble of downscaled CMIP6 models

Опубликована: Апрель 21, 2025

Extreme precipitation events are widely held to become more intense and frequent as a result of climate change, which will have major impacts for future flooding with implications the environment, infrastructure, agriculture, human life. We investigated projected changes daily mean, moderately extreme (99th 99.7th percentile), rare (Annual Exceedance Probability (AEP) 1 in 10, 50, 100) across Australia its greater capital cities, where approximately two thirds Australian population reside. used dynamically downscaled CMIP6 simulations from 4 modelling groups Australia. This large ensemble consists 19 different host models using 3 distinct regional 5 configurations, making an 39 simulations. The mean were quantified at each grid cell according rate change per degree global warming. largest increases extremes seen over northern Australia, 100 AEP event Darwin increase by 11.9% K^{− 1} 12.2% averages, respectively. Other cities had lower but still substantial (7.6% Brisbane, 7.3% Sydney, 3.4% Melbourne, 4.4% Perth). Large spatial differences noted among ensembles, showing varying patterns magnitudes change. These results highlight influence downscaling approach determining show need consider ensembles ensure uncertainties methods can be accounted for. findings inform decision around flood management, urban planning, water supply agriculture addition revealing globally relevant scientific insights.

Язык: Английский

CMIP6-driven 10 km super-resolution daily climate projections with PET estimates in China

Scientific Data, Год журнала: 2025, Номер 12(1)

Опубликована: Апрель 30, 2025

Global warming has intensified extreme weather events, posing challenges to regional climate and hydro-ecological systems. To address the low-resolution limitations of current multi-climate variables potential evapotranspiration (PET), this study develops a super-resolution fusion framework based on deep residual attention mechanisms, establishing China's 10-km resolution multi-model-multi-scenario high-resolution PET dataset (SRCPCN10). The Residual Channel Attention Network (RCAN) demonstrates exceptional downscaling performance for temperature, radiation, pressure (R2/KGE > 0.99), while precipitation exhibits significantly lower accuracy (R2 = 0.897) due spatial discontinuity. findings reveal distinct emission-gradient responses in future under SSP scenarios, with increases escalating alongside radiative forcing intensification. comparison annual mean differences between original CMIP6 downscaled data showed excellent agreement, most indices differing by less than 1%. This work overcomes traditional limitations, providing kilometer-scale multivariate watershed hydrological modeling, agricultural risk assessment, carbon-neutral pathway optimization, enhancing precision adaptation strategies.

Язык: Английский