Search

Dynamical downscaling is a widely‐used approach for generating regional projections of climate extremes at a finer scale. However, the systematic bias of the global climate model (GCM) generally degrades the reliability of projections. Recently, novel bias‐corrected CMIP6 data was generated using a mean‐variance‐trend (MVT) bias correction method for dynamical downscaling simulation. To validate the effectiveness of this data in the dynamical downscaling simulation of climate extremes, we carry out three Weather Research and Forecasting (WRF) simulations over Asia‐western North Pacific with a 25 km grid spacing from 1980 to 2014. The dynamical downscaling simulations driven by the raw GCM data set (hereafter WRF_GCM) and the bias‐corrected GCM (hereafter WRF_GCMbc) were assessed against the simulation driven by the European Center for Medium‐Range Weather Forecasts Reanalysis 5 data set. The results indicate that the MVT bias correction significantly improves the climatological mean and inter‐annual variability of downscaled climate extreme indices. In terms of the climatological mean, the WRF_GCMbc shows a 25%–82% decrease in root mean square errors (RMSEs) against the WRF_GCM. As for the inter‐annual variability, the MVT bias correction can improve the downscaling simulation of almost all precipitation extreme indices and 70% of the temperature extreme indices, characterized by the RMSEs' reduction of 1%–58%. The improvements of the climate extremes in terms of the climatological mean in the WRF_GCMbc primarily stem from the improved large‐scale circulation and ocean evaporation in the WRF, which in turn improves the downscaled precipitation and 2 m temperature through the advection, radiation, and surface energy exchange process. Plain Language Summary: High‐resolution projection of climate extremes is critical for climate risk assessment and regional adaptation planning. Regional climate model (RCM) is widely used to project future changes of climate extremes at a finer scale. However, as the input data for RCM, the global climate model (GCM) suffers from systematic biases, which greatly reduces the reliability of climate extremes' projections. Using the raw GCM data and the bias‐corrected GCM data by the mean‐variance‐trend (MVT) bias correction method, we conduct RCM simulations over the Asian‐western North Pacific region. We found that the MVT method can significantly improve the RCM simulations of various indices for precipitation and temperature extremes, reducing the overall bias by 1%–82%. Bias‐corrected GCM data contributes to better simulation of the large‐scale circulation in the RCM and further improves the simulation of climate extremes by various physical processes. Our results highlight the effectiveness of the bias‐corrected GCM data by the MVT method in RCM simulation of climate extremes. Key Points: Global climate model (GCM) bias should be constrained in the future projections of regional climate extremes by dynamical downscalingMultivariate bias correction method for GCM can greatly improve the downscaled simulation of climate extremesGCM bias corrections promote better simulations of the large‐scale circulation, resulting in improvements in downscaled climate extremes [ABSTRACT FROM AUTHOR]

High‐frequency daily variability (HFDV), as a fundamental aspect of climate variability, has drawn increasing attention due to its crucial impacts on public health, regional ecosystems and economic growth rates. The statistical metrics are of great importance to measure the changes in HFDV. In this study, we compared two common statistical metrics of monthly HFDV, including the standard deviation (SD) and the day‐to‐day difference (DTDx), for various variables (temperature, specific humidity and horizontal wind) using the NCEP/NCAR reanalysis dataset. Here, we show that the SD of daily variables includes mainly sub‐semiannual variability (<180 days), while DTDx can separate the synoptic variability (<12 days, DTD1), submonthly variability (<25 days, DTD3) and subseasonal variability (<60 days, DTD5) from other temporal scales. Moreover, a wider dominant timescale of DTDx causes a higher climatological mean and a closer relationship with SD. We also find that the linear trend in SD exhibits remarkable differences from that of the DTDx nearly all over the world, which can be explained by the significant change in the autocorrelation with different lag days under climate change. Our findings highlight that the DTDx is superior to SD in measuring HFDV, especially in a changing climate. [ABSTRACT FROM AUTHOR]