Articles | Volume 19, issue 14
https://doi.org/10.5194/gmd-19-6991-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-6991-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spectral nudging impacts on precipitation downscaling in the Conformal Cubic Atmospheric Model, version CCAM-2504: insights from summer 2011
The Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
Marcus J. Thatcher
The Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
Phuong Loan Nguyen
Climate Change Research Centre, UNSW Sydney, Sydney, NSW, Australia
Climate & Atmospheric Science, NSW Department of Climate Change, Energy, the Environment and Water, Sydney, NSW, Australia
Lisa V. Alexander
Climate Change Research Centre, UNSW Sydney, Sydney, NSW, Australia
ARC Centre of Excellence for the Weather of the 21st Century, UNSW Sydney, Sydney, NSW, Australia
John L. McGregor
The Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
Related authors
Phuong Loan Nguyen, Lisa V. Alexander, Thanh Ngo-Duc, Faye Cruz, Jerasorn Santisirisomboon, Liew Juneng, Donaldi S. Permana, Jing Xiang Chung, Julie Mae Dado, John L. McGregor, Grace Redmond, Tse Wai Po, Fredolin Tangang, Tan Phan-Van, Son C. H. Truong, Marcus Thatcher, Long Trinh-Tuan, Ummu Ma’rufah, Jennifer Tibay, Giovanni Di Virgilio, and Stephen White
EGUsphere, https://doi.org/10.5194/egusphere-2026-1325, https://doi.org/10.5194/egusphere-2026-1325, 2026
Short summary
Short summary
We introduce an ensemble of climate models that simulate Southeast Asia's future climate for 1960–2100. We (1) showed how well these models simulate observed climate by comparison with multiple observations, (2) applied a standardized benchmarking framework to model outputs to select a subset of models for further dynamical downscaling at kilometre-scale over megacities of SEA. These international efforts can help guide climate model design and the use and interpretation of climate projections.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Short summary
We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Linyuan Sun, Andréa S. Taschetto, Shayne McGregor, Lisa V. Alexander, and Chenhui Jin
EGUsphere, https://doi.org/10.5194/egusphere-2026-3344, https://doi.org/10.5194/egusphere-2026-3344, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
The El Niño-Southern Oscillation strongly affects Australia’s rainfall, but its impacts on Australian extratropical cyclones are difficult to detect. By focusing on days when ENSO-related teleconnection patterns were established, we identified clearer changes in cyclone activity over the Tasman Sea, with more frequent cyclones during La Niña and fewer during El Niño. These results provide new insights into how large-scale climate modes influence regional weather systems.
Phuong Loan Nguyen, Lisa V. Alexander, Thanh Ngo-Duc, Faye Cruz, Jerasorn Santisirisomboon, Liew Juneng, Donaldi S. Permana, Jing Xiang Chung, Julie Mae Dado, John L. McGregor, Grace Redmond, Tse Wai Po, Fredolin Tangang, Tan Phan-Van, Son C. H. Truong, Marcus Thatcher, Long Trinh-Tuan, Ummu Ma’rufah, Jennifer Tibay, Giovanni Di Virgilio, and Stephen White
EGUsphere, https://doi.org/10.5194/egusphere-2026-1325, https://doi.org/10.5194/egusphere-2026-1325, 2026
Short summary
Short summary
We introduce an ensemble of climate models that simulate Southeast Asia's future climate for 1960–2100. We (1) showed how well these models simulate observed climate by comparison with multiple observations, (2) applied a standardized benchmarking framework to model outputs to select a subset of models for further dynamical downscaling at kilometre-scale over megacities of SEA. These international efforts can help guide climate model design and the use and interpretation of climate projections.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Short summary
We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Cited articles
Alexander, L. V., Bador, M., Roca, R., Contractor, S., Donat, M. G., and Nguyen, P. L.: Intercomparison of annual precipitation indices and extremes over global land areas from in situ, space-based and reanalysis products, Environ. Res. Lett., 15, 055002, https://doi.org/10.1088/1748-9326/ab79e2, 2020.
Alexander, L. V., Nguyen, P. L., Donat, M. G., Dunn, R. J. H., Tett, S., Zhang, X., Alves, L. M., Bador, M., Deng, X., Gibson, P. B., King, A., Lennard, C., Min, S., Roca, R., and Trewin, B.: Less Intense Daily Precipitation Maxima in Regional Compared to Global Gridded Products, J. Climate, 38, 7669–7693, https://doi.org/10.1175/JCLI-D-25-0222.1, 2025.
Alexandru, A., de Elía, R., Laprise, R., Separovic, L., and Biner, S.: Sensitivity study of regional climate model simulations to large-scale nudging parameters, Mon. Weather Rev., 137, 1666–1686, https://doi.org/10.1175/2008MWR2620.1, 2009.
BoM: Record-breaking La Niña events: An analysis of the La Niña life cycle and the impacts and significance of the 2010–11 and 2011–12 La Niña events in Australia, Bureau of Meteorology, Commonwealth of Australia, https://www.bom.gov.au/climate/enso/history/La-Nina-2010-12.pdf (last access: 27 July 2026), 2012
Bullock Jr., O. R., Foroutan, H., Gilliam, R. C., and Herwehe, J. A.: Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0), Geosci. Model Dev., 11, 2897–2922, https://doi.org/10.5194/gmd-11-2897-2018, 2018.
Cai, W. and van Rensch, P.: The 2011 southeast Queensland extreme summer rainfall: A confirmation of a shift in the interdecadal Pacific oscillation?, Geophys. Res. Lett., 39, L08702, https://doi.org/10.1029/2011GL050820, 2012.
Cha, D. H., Jin, C. S., Lee, D. K., and Kuo, Y. H.: Impact of intermittent spectral nudging on regional climate simulation using WRF, J. Geophys. Res.-Atmos., 116, D10103, https://doi.org/10.1029/2010JD015069, 2011.
Chapman, S., Syktus, J., Trancoso, R., Thatcher, M., Toombs, N., Wong, K. K.-H., and Takbash, A.: Evaluation of dynamically downscaled CMIP6-CCAM models over Australia, Earth's Future, 11, e2023EF003548, https://doi.org/10.1029/2023EF003548, 2023.
Chapman, S., Syktus, J., Trancoso, R., Toombs, N., and Eccles, R.: Projected changes in mean climate and extremes from downscaled high-resolution CMIP6 simulations in Australia, Weather Clim. Extremes, 46, 100733, https://doi.org/10.1016/j.wace.2024.100733, 2024.
Choi, S. J. and Lee, D. K.: Impact of spectral nudging on the downscaling of tropical cyclones in regional climate simulations, Adv. Atmos. Sci., 33, 730–742, https://doi.org/10.1007/s00376-016-5061-y, 2015.
Chouinard, C., Béland, M., and McFarlane, N.: A Simple Gravity Wave Drag Parametrization for Use in Medium-Range Weather Forecast Models, Atmos.-Ocean, 24, 91–110, https://doi.org/10.1080/07055900.1986.9649242, 1986.
Evans, J. P., Boyer-Souchet, I., and Olson, R.: Local sea surface temperatures add to extreme precipitation in northeast Australia during La Niña, Geophys. Res. Lett., 39, L10803, https://doi.org/10.1029/2012GL052014, 2012.
Feser, F. and Barcikowska, M.: The influence of spectral nudging on typhoon formation in regional climate models, Environ. Res. Lett., 7, 014024, https://doi.org/10.1088/1748-9326/7/1/014024, 2012.
Freidenreich, S. M. and Ramaswamy, V.: A New Multiple-Band Solar Radiative Parameterization for General Circulation Models, J. Geophys. Res.-Atmos., 104, 31389–31409, https://doi.org/10.1029/1999JD900456,1999.
Gibson, P. B., Stuart, S., Sood, A., Stone, D., Rampal, N., Lewis, H., Broadbent, A., Thatcher, M., and Morgenstern, O.: Dynamical downscaling CMIP6 models over New Zealand: added value of climatology and extremes, Clim. Dyn., 62, 8255–8281, https://doi.org/10.1007/s00382-024-07337-5, 2024.
Gibson, P. B., Lewis, H., Campbell, I., Rampal, N., Fauchereau, N., and Harrington, L. J.: Downscaled climate projections of tropical and ex-tropical cyclones over the southwest Pacific, J. Geophys. Res.-Atmos., 130, e2025JD043833, https://doi.org/10.1029/2025JD043833, 2025.
Giles, B. D.: The Australian Summer 2010/2011, Weather, 67, 9–12, https://doi.org/10.1002/wea.860, 2012.
Giorgi, F.: Thirty years of regional climate modeling: Where are we and where are we going next?, J. Geophys. Res.-Atmos., 124, 5696–5723, https://doi.org/10.1029/2018JD030094, 2019.
Giorgi, F. and Mearns, L. O.: Introduction to special section: Regional climate modeling revisited, J. Geophys. Res.-Atmos., 104, 6335–6352, https://doi.org/10.1029/98JD02072, 1999.
Gómez, B. and Miguez-Macho, G.: The impact of wave number selection and spin-up time in spectral nudging, Q. J. Roy. Meteor. Soc., 143, 1772–1786, https://doi.org/10.1002/qj.3032, 2017.
Heikkila, U., Sandvik, A., and Sorteberg, A.: Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model, Clim. Dyn., 37, 1551–1564, https://doi.org/10.1007/s00382-010-0928-6, 2010.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Hoffmann, P., Katzfey, J. J., McGregor, J. L., and Thatcher, M.: Bias and variance correction of sea surface temperatures used for dynamical downscaling, J. Geophys. Res.-Atmos., 121, 12877–12890, https://doi.org/10.1002/2016JD025383, 2016.
Hong, S. Y. and Chang, E. C.: Spectral nudging sensitivity simulations in a regional climate model, Asia-Pac. J. Atmos. Sci., 48, 345–355, https://doi.org/10.1007/s13143-012-0033-3, 2012.
Howard, E., Su, C.-H., Stassen, C., Naha, R., Ye, H., Pepler, A., Bell, S. S., Dowdy, A. J., Tucker, S. O., and Franklin, C.: Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1, Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, 2024.
Huang, Z., Zhong, L., Ma, Y., and Fu, Y.: Development and evaluation of spectral nudging strategy for the simulation of summer precipitation over the Tibetan Plateau using WRF (v4.0), Geosci. Model Dev., 14, 2827–2841, https://doi.org/10.5194/gmd-14-2827-2021, 2021.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 1 day 0.1 degree × 0.1 degree V06, in: Goddard Earth Sciences Data and Information Services Center (GES DISC), edited by: Savtchenko, A. and Greenbelt, M. D., https://doi.org/10.5067/GPM/IMERGDF/DAY/06, 2019.
Huffman, G. J., Behrangi, A., Bolving, D. T., and Nelkin, E. J.: GPCP Version 3.2 Daily Precipitation Data Set, edited by: Huffman, G. J., Behrangi, A. Bolvin, D. T., and Nelkin, E. J., Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/MEASURES/GPCP/DATA305, 2022.
Huffman, G. J., Adler, R. F., Behrangi, A., Bolvin, D. T., Nelkin, E. J., Gu, G., and Ehsani, M. R.: The New Version 3.2 Global Precipitation Climatology Project (GPCP) Monthly and Daily Precipitation Products, J. Climate, 36, 7635–7655, https://doi.org/10.1175/JCLI-D-23-0123.1, 2023.
Hurley, P.: Modelling Mean and Turbulence Fields in the Dry Convective Boundary Layer With the Eddy-Diffusivity/Mass-Flux Approach, Bound.-Layer Meteorol., 125, 525–536, https://doi.org/10.1007/s10546-007-9203-8, 2007.
Imran, H. M. and Evans, J. P.: Observational uncertainty in the added value of regional climate modelling over Australia, Clim. Dyn., 63, 73, https://doi.org/10.1007/s00382-024-07562-y, 2025.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, https://doi.org/10.1017/9781009157896, 2021.
Isphording, R. N., Alexander, L. V., Bador, M., Green, D., Evans, J. P., and Wales, S.: A standardized benchmarking framework to assess downscaled precipitation simulations, J. Climate, 37, 1089–1110, https://doi.org/10.1175/JCLI-D-23-0317.1, 2024.
Jin, C. S., Cha, D. H., Lee, D. K., Suh, M. S., Hong, S. Y., Kang, H. S., and Ho, C. H.: Evaluation of climatological tropical cyclone activity over the western North Pacific in the CORDEX-East Asia multi-RCM simulations, Clim. Dyn., 47, 765–778, https://doi.org/10.1007/s00382-015-2869-6, 2016.
Jones, D. A., Wang, W., and Fawcett, R.: High-quality spatial climate datasets for Australia, Aust. Meteor. Oceanogr. J., 58, 233–248, https://doi.org/10.22499/2.5804.003, 2009.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeor., 5, 487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
Kanamitsu, M. and Kanamaru, H.: Fifty-seven-Year California reanalysis downscaling at 10 km (CaRD10), Part I: System detail and validation with observations, J. Climate, 20, 5553–5571, https://doi.org/10.1175/2007JCLI1482.1, 2007.
Karoly, D. J. and Boulter, S.: Afterword: Floods, storms, fires and pestilence – disaster risk in Australia during 2010–2011, in: Natural Disasters and Adaptation to Climate Change, edited by: Boulter, S., Palutikof, J., Karoly, D. J., and Guitart, D., Cambridge University Press, Cambridge, 252–261, https://doi.org/10.1017/CBO9780511845710.031, 2013.
Kowalczyk, E. A., Wang, Y. P., and Law, R. M.: The CSIRO Atmospheric Biosphere Land Exchnage (CABLE) model for use in climate models and as an offline model, CSIRO Marine and Atmospheric Research Paper no. 13, http://www.cmar.csiro.au/e-print/open/kowalczykea_2006a.pdf (last access: 27 July 2026), 2006.
Lai, W. and Gan, J.: On spectral nudging and dynamics to improve representation of marine cloud and precipitation over the China Sea in summer, Theor. Appl. Climatol., 156, 444, https://doi.org/10.1007/s00704-025-05689-4, 2025.
Liang, X.-Z., Kunkel, K. E., Meehl, G. A., Jones, R. G., and Wang, J. X. L.: Regional climate models downscaling analysis of general circulation models present climate biases propagation into future change projections, Geophys. Res. Lett., 35, L08709, https://doi.org/10.1029/2007GL032849, 2008.
Lisonbee, J. and Ribbe, J.: Seasonal climate influences on the timing of the Australian monsoon onset, Weather Clim. Dynam., 2, 489–506, https://doi.org/10.5194/wcd-2-489-2021, 2021.
Liu, P., Tsimpidi, A. P., Hu, Y., Stone, B., Russell, A. G., and Nenes, A.: Differences between downscaling with spectral and grid nudging using WRF, Atmos. Chem. Phys., 12, 3601–3610, https://doi.org/10.5194/acp-12-3601-2012, 2012.
Liu, S., Zeman, C., and Schär, C.: Dynamical downscaling of climate simulations in the tropics, Geophys. Res. Lett., 51, e2023GL105733, https://doi.org/10.1029/2023GL105733, 2024.
Liu, Y. L., Alexander, L. V., Evans, J. P., and Thatcher, M.: Sensitivity of Australian rainfall to driving SST datasets in a variable-resolution global atmospheric model, J. Geophys. Res.-Atmos., 129, e2024JD040954, https://doi.org/10.1029/2024JD040954, 2024.
Ma, S., Trancoso, R., Syktus, J., Chapman, S., and Eccles, R.: Evaluating ERA5 downscaled simulations using CCAM: Large-scale circulation processes and teleconnections, J. Geophys. Res.-Atmos., 130, e2025JD043566, https://doi.org/10.1029/2025JD043566, 2025.
Mai, X., Qiu, X., Yang, Y., and Ma, Y.: Impacts of spectral nudging parameters on dynamical downscaling in summer over Mainland China, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.574754, 2020.
McGregor, J. L.: A New Convection Scheme Using a Simple Closure, BMRC Research Report 93, Bureau of Meteorology Research Centre, Melbourne, 33–36, https://research.csiro.au/ccam/wp-content/uploads/sites/520/2024/01/1377337417.pdf (last access: 27 July 2026), 2003.
McGregor, J. L. and Dix, M. R.: An Updated Description of the Conformal-Cubic Atmospheric Model, in: High Resolution Numerical Modelling of the Atmosphere and Ocean, edited by: Hamilton, K. and Ohfuchi, W., Springer, https://doi.org/10.1007/978-0-387-49791-4_4, 2008.
Menut, L., Bessagnet, B., Cholakian, A., Siour, G., Mailler, S., and Pennel, R.: What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?, Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, 2024.
Miguez-Macho, G., Stenchikov, G. L., and Robock, A.: Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations, J. Geophys. Res., 109, D13104, https://doi.org/10.1029/2003JD004495, 2004.
Narsey, S., Grose, M., Delage, F., Tolhurst, G., Chung, C., Takbash, A., Boschat, G., King, M., Pepler, A., Thatcher, M., Ng, B., Truong, S., Su, C., Howard, E., Stassen, C., Black, M., Jones, D., Matear, R., Chapman, S., Syktus, J., Trancoso, R., Di Virgilio, G., Goyal, R., Kala, J., Round, V., and Evans, J. P.: Disentangling the uncertainties in regional projections for Australia, J. South. Hemisph. Earth Syst. Sci., 75, ES25015, https://doi.org/10.1071/ES25015, 2025.
Nguyen, P.-L., Bador, M., Alexander, L. V., Lane, T. P., and Funk, C. C.: On the Robustness of Annual Daily Precipitation Maxima Estimates Over Monsoon Asia, Front. Clim., 2, 578785, https://doi.org/10.3389/fclim.2020.578785, 2020.
Nguyen, P.-L., Bador, M., Alexander, L. V., Lane, T. P., and Ngo-Duc, T.: More intense daily precipitation in CORDEX-SEA regional climate models than their forcing global climate models over Southeast Asia, Int. J. Climatol., 42, 6537–6561, https://doi.org/10.1002/joc.7619, 2022.
Nguyen, P. L., Alexander, L. V., Thatcher, M. J., Truong, S. C. H., Isphording, R. N., and McGregor, J. L.: Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework, Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, 2024.
Omrani, H., Drobinski, P., Dubos, T., and Turuncoglu, U.: Optimal nudging strategies in regional climate modeling: Investigation in a Big-Brother simulation over the Euro-Mediterranean region, Clim. Dyn., 44, 1559–1577, https://doi.org/10.1007/s00382-014-2453-5, 2015.
Otte, T. L., Nolte, C. G., Otte, M. J., and Bowden, J. H.: Does Nudging Squelch the Extremes in Regional Climate Modeling?, J. Climate, 25, 7046–7066, https://doi.org/10.1175/JCLI-D-12-00048.1, 2012.
Rotstayn, L. D.: A Physically Based Scheme for the Treatment of Stratiform Clouds and Precipitation in Large-Scale Models. I: Description and Evaluation of the Microphysical Processes, Q. J. Roy. Meteor. Soc., 123, 1227–1282, 1997.
Schroeter, B. J. E., Ng, B., Takbash, A., Rafter, T., and Thatcher, M.: A Comprehensive Evaluation of Mean and Extreme Climate for the Conformal Cubic Atmospheric Model (CCAM), J. Appl. Meteor. Climatol., 63, 997–1018, https://doi.org/10.1175/JAMC-D-24-0004.1, 2024.
Schwarzkopf, M. D. and Ramaswamy, V.: Radiative Effects of CH4, N2O, Halocarbons and the Foreign-Broadened H2O Continuum: A GCM Experiment, J. Geophys. Res.-Atmos., 104, 9467–9488, https://doi.org/10.1029/1999JD900003,1999.
Spero, T. L., Otte, M. J., Bowden, J. H., and Nolte, C. G.: Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model, J. Geophys. Res.-Atmos., 119, 11682–11694, https://doi.org/10.1002/2014JD022173, 2014.
Spero, T. L., Nolte, C. G., Bowden, J. H., and Mallard, M. S.: Sensitivity of WRF simulations to different spectral nudging techniques, J. Appl. Meteor. Climatol., 57, 1303–1320, https://doi.org/10.1175/JAMC-D-17-0360.1, 2018.
Su, C., Torrance, J., Rennie, S., Howard, E., Stassen, C., Warren, R., Smith, A., Dharssi, I., Pepler, A., Tian, S., Lipson, M., Steinle, P., Franklin, C., Le, T., Wang, C., Masoumi, S., and Le Marshall, J.: The Australian regional atmospheric reanalysis system, version 2 – BARRA2, J. South. Hemisph. Earth Syst. Sci., 75, ES25032, https://doi.org/10.1071/ES25032, 2025.
Tang, J., Wang, S., Niu, X., Hui, P., Zong, P., and Wang, X.: Impact of spectral nudging on regional climate simulation over CORDEX East Asia using WRF, Clim. Dyn., 48, 2339–2357, https://doi.org/10.1007/s00382-016-3208-2, 2017.
Thatcher, M. and Hurley, P.: Simulating Australian Urban Climate in a Mesoscale Atmospheric Numerical Model, Bound.-Layer Meteorol., 142, 149–175, https://doi.org/10.1007/s10546-011-9663-8, 2012.
Thatcher, M. and McGregor, J. L.: Using a scale-selective filter for dynamical downscaling with the conformal cubic atmospheric model, Mon. Weather Rev., 136, 4578–4596, https://doi.org/10.1175/2008MWR2599.1, 2008.
Truong, C. H. S.: Model code for “Spectral Nudging Impacts on Precipitation Downscaling in the Conformal Cubic Atmospheric Model, version CCAM-2504: Insights from Summer 2011”, Zenodo [code], https://doi.org/10.5281/zenodo.19018138, 2026a.
Truong, C. H. S.: Datasets for “Spectral Nudging Impacts on Precipitation Downscaling in the Conformal Cubic Atmospheric Model, version CCAM-2504: Insights from Summer 2011”, Zenodo [data set], https://doi.org/10.5281/zenodo.19077484, 2026b.
Truong, C. H. S.: Python code for “Spectral Nudging Impacts on Precipitation Downscaling in the Conformal Cubic Atmospheric Model, version CCAM-2504: Insights from Summer 2011”, Zenodo [code], https://doi.org/10.5281/zenodo.18423589, 2026c.
Truong, S. C. H. and Thatcher, M.: Evaluation of clouds in the conformal cubic atmospheric model using the CFMIP Observation Simulator Package, Int. J. Climatol., 45, e8846, https://doi.org/10.1002/joc.8846, 2025.
Truong, S. C. H., Huang, Y., Siems, S. T., Manton, M. J., and Lang, F.: Biases in the thermodynamic structure over the Southern Ocean in ERA5 and their radiative implications, Int. J. Climatol., 42, 7685–7702, https://doi.org/10.1002/joc.7672, 2022.
Truong, S. C. H., Ramsay, H. A., Rafter, T., and Thatcher, M. J.: Simulation of an intense tropical cyclone in the conformal cubic atmospheric model and its sensitivity to horizontal resolution, Weather Clim. Extremes, 47, 100744, https://doi.org/10.1016/j.wace.2025.100744, 2025.
Ummenhofer, C. C., Sen Gupta, A., England, M. H., Taschetto, A. S., Briggs, P. R., and Raupach, M. R.: How did ocean warming affect Australian rainfall extremes during the 2010–2011 La Niña event?, Geophys. Res. Lett., 42, 9942–9951, https://doi.org/10.1002/2015GL065948, 2015.
Virman, M., Bister, M., Räisänen, J., Sinclair, V. A., and Järvinen, H.: Radiosonde comparison of ERA5 and ERA-Interim reanalysis datasets over tropical oceans, Tellus A, 73, 1–7, https://doi.org/10.1080/16000870.2021.1929752, 2021.
von Storch, H., Langenberg, H., and Feser, F.: A spectral nudging technique for dynamical downscaling purposes, Mon. Weather Rev., 128, 3664–3673, https://doi.org/10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2, 2000.
Wang, J. and Kotamarthi, V. R.: Assessment of dynamical downscaling in near-surface fields with different spectral nudging approaches using the nested regional climate model (NRCM), J. Appl. Meteor. Climatol., 52, 1576–1591, https://doi.org/10.1175/JAMC-D-12-0302.1, 2013.
Xie, P., Joyce, R., Wu, S., Yoo, S., Yarosh, Y., Sun, F., and Lin, R.: Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998, J. Hydrometeor., 18, 1617–1641, https://doi.org/10.1175/JHM-D-16-0168.1, 2017.
Xie, P., Joyce, R., Wu, S., Yoo, S., Yarosh, Y., Sun, F., and Lin, R.: NOAA Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1, NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/w9va-q159, 2019.
Yang, L., Wang, S., Tang, J., Niu, X., and Fu, C.: Impact of Nudging Parameters on Dynamical Downscaling over CORDEX East Asia Phase II Domain: The Case of Summer 2003, J. Appl. Meteor. Climatol., 58, 2755–2771, https://doi.org/10.1175/JAMC-D-19-0152.1, 2019.
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh, A.: APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges, Bull. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1, 2012.
Zhang, H., Chapman, S., Trancoso, R., Toombs, N., and Syktus, J.: Assessing the impact of bias correction approaches on climate extremes and the climate change signal, Meteorol. Appl., 31, e2204, https://doi.org/10.1002/met.2204, 2024.
Short summary
Understanding future rainfall is essential for managing floods and water resources in Australia. We tested different ways of helping a regional climate model better represent large-scale weather patterns during the extreme 2010–2011 La Niña event and compared the results with observations. The best approach produced more realistic atmospheric conditions and rainfall, increasing confidence in future climate projections and helping improve information for planning and climate adaptation
Understanding future rainfall is essential for managing floods and water resources in Australia....