Qianyu Zha

The UKCP18 Convection-permitting model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localised storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we apply and evaluate a quantile mapping (QM) bias correction method for UKCP18-CPM hourly precipitation (with diurnal correction) and daily temperature over England. We quantify how closely the bias corrected simulations align with observations relative to the raw simulations. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R2 values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R2 = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1 %–39.1 % and mean daily temperature by 3.1 to 4.5 °C, highlighting the potential for more intense climate-related events. Overall, the applied bias-correction method brings UKCP18-CPM simulations into closer agreement with observations for both mean behaviour and extremes, providing a more reliable basis for high-resolution impact modelling and assessments that require hourly precipitation forcing.

The UKCP18 Convection-Permitting Model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localized storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we applied a quantile mapping (QM) method to correct hourly precipitation and daily temperature for four selected ensemble members (EM01, EM04, EM07, EM08) of the UKCP18-CPM for England. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 °C to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R² values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R² = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1–39.1 % and mean daily temperature by 3.1 °C to 4.5 °C, highlighting the potential for more intense climate-related events. These results emphasize the effectiveness of bias correction in reducing model biases and improving the reliability of the CPM climate projections, thereby supporting more reliable future high-resolution climate and hydrological impact assessments in England.

The UKCP18 Convection-Permitting Model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localized storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we applied a quantile mapping (QM) method to correct hourly precipitation and daily temperature for four selected ensemble members (EM01, EM04, EM07, EM08) of the UKCP18-CPM for England. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 °C to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R² values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R² = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1–39.1 % and mean daily temperature by 3.1 °C to 4.5 °C, highlighting the potential for more intense climate-related events. These results emphasize the effectiveness of bias correction in reducing model biases and improving the reliability of the CPM climate projections, thereby supporting more reliable future high-resolution climate and hydrological impact assessments in England.

The UKCP18 Convection-Permitting Model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localized storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we applied a quantile mapping (QM) method to correct hourly precipitation and daily temperature for four selected ensemble members (EM01, EM04, EM07, EM08) of the UKCP18-CPM for England. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 °C to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R² values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R² = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1–39.1 % and mean daily temperature by 3.1 °C to 4.5 °C, highlighting the potential for more intense climate-related events. These results emphasize the effectiveness of bias correction in reducing model biases and improving the reliability of the CPM climate projections, thereby supporting more reliable future high-resolution climate and hydrological impact assessments in England.

The UKCP18 Convection-Permitting Model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localized storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we applied a quantile mapping (QM) method to correct hourly precipitation and daily temperature for four selected ensemble members (EM01, EM04, EM07, EM08) of the UKCP18-CPM for England. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 °C to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R² values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R² = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1–39.1 % and mean daily temperature by 3.1 °C to 4.5 °C, highlighting the potential for more intense climate-related events. These results emphasize the effectiveness of bias correction in reducing model biases and improving the reliability of the CPM climate projections, thereby supporting more reliable future high-resolution climate and hydrological impact assessments in England.

The UKCP18 Convection-Permitting Model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localized storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we applied a quantile mapping (QM) method to correct hourly precipitation and daily temperature for four selected ensemble members (EM01, EM04, EM07, EM08) of the UKCP18-CPM for England. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 °C to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R² values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R² = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1–39.1 % and mean daily temperature by 3.1 °C to 4.5 °C, highlighting the potential for more intense climate-related events. These results emphasize the effectiveness of bias correction in reducing model biases and improving the reliability of the CPM climate projections, thereby supporting more reliable future high-resolution climate and hydrological impact assessments in England.

The United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km regional perturbed physics ensemble (UKCP18-RCM-PPE) is one of the three strands of the latest set of UK national climate projections produced by the UK Met Office. It has been widely adopted in climate impact assessment. In this study, we report biases in the raw UKCP18-RCM simulations that are significant and are likely to deteriorate impact assessments if they are not adjusted. Two methods were used to bias-correct UKCP18-RCM: non-parametric quantile mapping using empirical quantiles and a variant developed for the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) designed to preserve the climate change signal. Specifically, daily temperature and precipitation simulations for 1981 to 2080 were adjusted for the 12 ensemble members. Potential evapotranspiration was also estimated over the same period using the Penman–Monteith formulation and then bias-corrected using the latter method. Both methods successfully corrected biases in a range of daily temperature, precipitation, and potential evapotranspiration metrics and reduced biases in multi-day precipitation metrics to a lesser degree. An exploratory analysis of the projected future changes confirms the expectation of wetter, warmer winters and hotter, drier summers and shows uneven changes in different parts of the distributions of both temperature and precipitation. Both bias-correction methods preserved the climate change signal almost equally well, as well as the spread among the projected changes. The change factor method was used as a benchmark for precipitation, and we show that it fails to capture changes in a range of variables, making it inadequate for most impact assessments. By comparing the differences between the two bias-correction methods and within the 12 ensemble members, we show that the uncertainty in future precipitation and temperature changes stemming from the climate model parameterization far outweighs the uncertainty introduced by selecting one of these two bias-correction methods. We conclude by providing guidance on the use of the bias-corrected datasets. The datasets bias-adjusted with ISIMIP3BA are publicly available in the following repositories: https://doi.org/10.5281/zenodo.6337381 for precipitation and temperature (Reyniers et al., 2022a) and https://doi.org/10.5281/zenodo.6320707 for potential evapotranspiration (Reyniers et al., 2022b). The datasets, bias-corrected using the quantile mapping method, are available at https://doi.org/10.5281/zenodo.8223024 (Zha et al., 2023).

The United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km regional perturbed physics ensemble (UKCP18-RCM-PPE) is one of the three strands of the latest set of UK national climate projections produced by the UK Met Office. It has been widely adopted in climate impact assessment. In this study, we report biases in the raw UKCP18-RCM simulations that are significant and are likely to deteriorate impact assessments if they are not adjusted. Two methods were used to bias-correct UKCP18-RCM: non-parametric quantile mapping using empirical quantiles and a variant developed for the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) designed to preserve the climate change signal. Specifically, daily temperature and precipitation simulations for 1981 to 2080 were adjusted for the 12 ensemble members. Potential evapotranspiration was also estimated over the same period using the Penman-Monteith formulation and then bias-corrected using the latter method. Both methods successfully corrected biases in a range of daily temperature, precipitation and potential evapotranspiration metrics, and reduced biases in multi-day precipitation metrics to a lesser degree. An exploratory analysis of the projected future changes confirms the expectation of wetter, warmer winters and hotter, drier summers, and shows uneven changes in different parts of the distributions of both temperature and precipitation. Both bias-correction methods preserved the climate change signal almost equally well, as well as the spread among the projected changes. The change factor method was used as a benchmark for precipitation, and we show that it fails to capture changes in a range of variables, making it inadequate for most impact assessments. By comparing the differences between the two bias-correction methods and within the 12 ensemble members, we show that the uncertainty in future precipitation and temperature changes stemming from the climate model parameterisation far outweighs the uncertainty introduced by selecting one of these two bias-correction methods. We conclude by providing guidance on the use of the bias-corrected data sets. The data sets bias adjusted with ISIMIP3BA are publicly available in the following repositories: https://doi.org/10.5281/zenodo.6337381 for precipitation and temperature (Reyniers et al., 2022a) and https://doi.org/10.5281/zenodo.6320707 for potential evapotranspiration (Reyniers et al., 2022b) . The datasets bias-corrected using the quantile mapping method are available at https://doi.org/10.5281/zenodo.8223024 (Zha et al., 2023) .

Assessment of climate change impacts on flooding risks has been undertaken by using hydrological models calibrated at a daily time step and driven by daily outputs from the global or regional climate models. However, the daily scale model typically underestimates the magnitude of floods. A model run at a higher temporal resolution can be more capable of capturing flood peaks and hence more representative of the expected future flood magnitude (Beylich et al., 2021; Huang et al., 2019). This study aims to develop an hourly HBV hydrological model for Great Britain. The precipitation observations at an hourly time step for Great Britain [CEH-GEAR1hr] (Lewis et al., 2022) were used to calibrate the hourly HBV model. The model was also calibrated using daily observations from HadUK-Grid dataset (Hollis et al., 2019). The CAMELS-GB catchments in Great Britain (Coxon et al., 2020) were selected as the study area. Hourly time series of flow data were obtained from Environment Agency (EA) for England, Scottish Environment Protection Agency (SEPA) for Scotland and Natural Resources Wales (NRW) for Wales. Daily flow data are from National River Flow Archive (NRFA). The calibrating objective function for the HBV hydrological model at both daily and hourly time steps is Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970), and also the modified Kling-Gupta efficiency (KGE), the ratio of the root-mean-square error to the standard deviation (RSR) and Pearson's correlation coefficient (r) were used to compare. For the daily model, more than 77% and 35% of the CAMELS-GB catchments achieve NSE values over 0.6 and 0.8, respectively. The hourly model performance is comparable with the daily model and the hourly model outperforms the daily model in capturing the peak flows.ReferencesBeylich, M., Haberlandt, U., Reinstorf, F., 2021. Daily vs. hourly simulation for estimating future flood peaks in mesoscale catchments. Hydrol. Res. 52, 821–833. Coxon, G., Addor, N., Bloomfield, J.P., Freer, J., Fry, M., Hannaford, J., Howden, N.J.K., Lane, R., Lewis, M., Robinson, E.L., Wagener, T., Woods, R., 2020. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. Earth Syst. Sci. Data 12, 2459–2483. Hollis, D., McCarthy, M., Kendon, M., Legg, T., Simpson, I., 2019. HadUK‐Grid—A new UK dataset of gridded climate observations. Geosci. Data J. 6, 151–159. Huang, Y., Bárdossy, A., Zhang, K., 2019. Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data. Hydrol. Earth Syst. Sci. 23, 2647–2663. Lewis, E., Quinn, N., Blenkinsop, S., Fowler, H.J., Freer, J., Tanguy, M., Hitt, O., Coxon, G., Bates, P., Woods, R., Fry, M., Chevuturi, A., Swain, O., White, S.M., 2022. Gridded estimates of hourly areal rainfall for Great Britain 1990-2016 [CEH-GEAR1hr] v2.Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — A discussion of principles. J. Hydrol. 10, 282–290.