Articles | Volume 18, issue 1
https://doi.org/10.5194/gmd-18-161-2025
© Author(s) 2025. 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-18-161-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
15 Jan 2025
Model experiment description paper |
| 15 Jan 2025
| 15 Jan 2025
Climate model downscaling in central Asia: a dynamical and a neural network approach
Bijan Fallah
CORRESPONDING AUTHOR
German Climate Computing Center (DKRZ), Hamburg, Germany
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Masoud Rostami
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Laboratoire de Météorologie Dynamique (LMD), Sorbonne University (SU), Ecole Normale Supérieure (ENS), Paris, France
Emmanuele Russo
ETH Zurich, Department of Environmental Systems Science, Universitätstrasse 16, 8092 Zurich, Switzerland
Paula Harder
Mila Quebec AI Institute, Montreal, Canada
Christoph Menz
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Peter Hoffmann
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Iulii Didovets
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Fred F. Hattermann
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Faculty of Forest and Environment, Eberswalde University for Sustainable Development (HNEE), Eberswalde, Germany
Related authors
Emmanuele Russo, Bijan Fallah, Patrick Ludwig, Melanie Karremann, and Christoph C. Raible
Clim. Past, 18, 895–909, https://doi.org/10.5194/cp-18-895-2022, https://doi.org/10.5194/cp-18-895-2022, 2022
Short summary
Short summary
In this study a set of simulations are performed with the regional climate model COSMO-CLM for Europe, for the mid-Holocene and pre-industrial periods. The main aim is to better understand the drivers of differences between models and pollen-based summer temperatures. Results show that a fundamental role is played by spring soil moisture availability. Additionally, results suggest that model bias is not stationary, and an optimal configuration could not be the best under different forcing.
Kai Schröter, Pia-Johanna Schweizer, Benedikt Gräler, Lydia Cumiskey, Sukaina Bharwani, Janne Parviainen, Chahan M. Kropf, Viktor Wattin Håkansson, Martin Drews, Tracy Irvine, Clarissa Dondi, Heiko Apel, Jana Löhrlein, Stefan Hochrainer-Stigler, Stefano Bagli, Levente Huszti, Christopher Genillard, Silvia Unguendoli, Fred Hattermann, and Max Steinhausen
Nat. Hazards Earth Syst. Sci., 25, 3055–3073, https://doi.org/10.5194/nhess-25-3055-2025, https://doi.org/10.5194/nhess-25-3055-2025, 2025
Short summary
Short summary
With the increasing negative impacts of extreme weather events globally, it is crucial to align efforts to manage disasters with measures to adapt to climate change. We identify challenges in systems and organizations working together. We suggest that collaboration across various fields is essential and propose an approach to improve collaboration, including a framework for better stakeholder engagement and an open-source data system that helps gather and connect important information.
Atabek Umirbekov, Mayra Daniela Peña-Guerrero, Iulii Didovets, Heiko Apel, Abror Gafurov, and Daniel Müller
Hydrol. Earth Syst. Sci., 29, 3055–3071, https://doi.org/10.5194/hess-29-3055-2025, https://doi.org/10.5194/hess-29-3055-2025, 2025
Short summary
Short summary
Seasonal streamflow forecasts for snowmelt-dominated catchments often rely on snowpack data, which are not always available and are prone to errors. Our study evaluates near-real-time global snow estimates and climate oscillation indices for predictions in the data-scarce mountains of central Asia. We show that climate indices can improve prediction accuracy at longer lead times, help offset snow data uncertainty, and enhance predictions where streamflow depends on in-season climate variability.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Short summary
Short summary
This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Basil A. S. Davis, Marc Fasel, Jed O. Kaplan, Emmanuele Russo, and Ariane Burke
Clim. Past, 20, 1939–1988, https://doi.org/10.5194/cp-20-1939-2024, https://doi.org/10.5194/cp-20-1939-2024, 2024
Short summary
Short summary
During the last ice age (21 000 yr BP) in Europe, the composition and extent of forest and its associated climate remain unclear, with models indicating more forest north of the Alps and a warmer and somewhat wetter climate than suggested by the data. A new compilation of pollen records with improved dating suggests greater agreement with model climates but still suggests models overestimate forest cover, especially in the west.
Emmanuele Russo, Jonathan Buzan, Sebastian Lienert, Guillaume Jouvet, Patricio Velasquez Alvarez, Basil Davis, Patrick Ludwig, Fortunat Joos, and Christoph C. Raible
Clim. Past, 20, 449–465, https://doi.org/10.5194/cp-20-449-2024, https://doi.org/10.5194/cp-20-449-2024, 2024
Short summary
Short summary
We present a series of experiments conducted for the Last Glacial Maximum (~21 ka) over Europe using the regional climate Weather Research and Forecasting model (WRF) at convection-permitting resolutions. The model, with new developments better suited to paleo-studies, agrees well with pollen-based climate reconstructions. This agreement is improved when considering different sources of uncertainty. The effect of convection-permitting resolutions is also assessed.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jonathan Robert Buzan, Emmanuele Russo, Woon Mi Kim, and Christoph C. Raible
EGUsphere, https://doi.org/10.5194/egusphere-2023-324, https://doi.org/10.5194/egusphere-2023-324, 2023
Preprint archived
Short summary
Short summary
Paleoclimate is used to test climate models to verify that simulations accurately project both future and past climate states. We present fully coupled climate sensitivity simulations of Preindustrial, Last Glacial Maximum, and the Quaternary climate periods. We show distinct climate states derived from non-linear responses to ice sheet heights and orbits. The implication is that as paleo proxy data become more reliable, they may constrain the specific climate states produced by climate models.
Emmanuele Russo, Bijan Fallah, Patrick Ludwig, Melanie Karremann, and Christoph C. Raible
Clim. Past, 18, 895–909, https://doi.org/10.5194/cp-18-895-2022, https://doi.org/10.5194/cp-18-895-2022, 2022
Short summary
Short summary
In this study a set of simulations are performed with the regional climate model COSMO-CLM for Europe, for the mid-Holocene and pre-industrial periods. The main aim is to better understand the drivers of differences between models and pollen-based summer temperatures. Results show that a fundamental role is played by spring soil moisture availability. Additionally, results suggest that model bias is not stationary, and an optimal configuration could not be the best under different forcing.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
Short summary
Short summary
We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Basit Khan, Sabine Banzhaf, Edward C. Chan, Renate Forkel, Farah Kanani-Sühring, Klaus Ketelsen, Mona Kurppa, Björn Maronga, Matthias Mauder, Siegfried Raasch, Emmanuele Russo, Martijn Schaap, and Matthias Sühring
Geosci. Model Dev., 14, 1171–1193, https://doi.org/10.5194/gmd-14-1171-2021, https://doi.org/10.5194/gmd-14-1171-2021, 2021
Short summary
Short summary
An atmospheric chemistry model has been implemented in the microscale PALM model system 6.0. This article provides a detailed description of the model, its structure, input requirements, various features and limitations. Several pre-compiled ready-to-use chemical mechanisms are included in the chemistry model code; however, users can also easily implement other mechanisms. A case study is presented to demonstrate the application of the new chemistry model in the urban environment.
Emmanuele Russo, Silje Lund Sørland, Ingo Kirchner, Martijn Schaap, Christoph C. Raible, and Ulrich Cubasch
Geosci. Model Dev., 13, 5779–5797, https://doi.org/10.5194/gmd-13-5779-2020, https://doi.org/10.5194/gmd-13-5779-2020, 2020
Short summary
Short summary
The parameter space of the COSMO-CLM RCM is investigated for the Central Asia CORDEX domain using a perturbed physics ensemble (PPE) with different parameter values. Results show that only a subset of model parameters presents relevant changes in model performance and these changes depend on the considered region and variable: objective calibration methods are highly necessary in this case. Additionally, the results suggest the need for calibrating an RCM when targeting different domains.
Cited articles
Ban, N., Schmidli, J., and Schär, C.: Heavy precipitation in a changing climate: Does short-term summer precipitation increase faster?, Geophys. Res. Lett., 42, 1165–1172, 2015. a
Chokkavarapu, N. and Mandla, V. R.: Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation, SN Applied Sciences, 1, 1698, https://doi.org/10.1007/s42452-019-1764-x, 2019. a
Ciarlo`, J. M., Coppola, E., Fantini, A., Giorgi, F., Gao, X., Tong, Y., Glazer, R. H., Torres Alavez, J. A., Sines, T., Pichelli, E., Raffaele, F., Das, S., Bukovsky, M., Ashfaq, M., Im, E.-S., Nguyen-Xuan, T., Teichmann, C., Remedio, A., Remke, T., Bülow, K., Weber, T., Buntemeyer, L., Sieck, K., Rechid, D., and Jacob, D.: A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles, Clim. Dynam., 57, 1403–1424, 2021. a, b, c
Cui, T., Li, C., and Tian, F.: Evaluation of temperature and precipitation simulations in CMIP6 models over the Tibetan Plateau, Earth Space Sci., 8, e2020EA001620, https://doi.org/10.1029/2020EA001620, 2021. a
Demory, M.-E., Berthou, S., Fernández, J., Sørland, S. L., Brogli, R., Roberts, M. J., Beyerle, U., Seddon, J., Haarsma, R., Schär, C., Buonomo, E., Christensen, O. B., Ciarlo ̀, J. M., Fealy, R., Nikulin, G., Peano, D., Putrasahan, D., Roberts, C. D., Senan, R., Steger, C., Teichmann, C., and Vautard, R.: European daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 13, 5485–5506, https://doi.org/10.5194/gmd-13-5485-2020, 2020. a
Didovets, I., Krysanova, V., Nurbatsina, A., Fallah, B., Krylova, V., Saparova, A., Niyazov, J., Kalashnikova, O., and Hattermann, F. F.: Attribution of current trends in streamflow to climate change for 12 Central Asian catchments, Clim. Change, 177, 16, https://doi.org/10.1007/s10584-023-03673-3, 2024. a
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022. a
Dosio, A. and Panitz, H.-J.: Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models, Clim. Dynam., 46, 1599–1625, 2016. a
Fallah, B.: Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach (1.0.0), Zenodo [software], https://doi.org/10.5281/zenodo.10417111, 2023. a, b
Fallah, B. and Rostami, M.: Exploring the impact of the recent global warming on extreme weather events in Central Asia using the counterfactual climate data ATTRICI v1.1, Clim. Change, 177, 80, https://doi.org/10.1007/s10584-024-03743-0, 2024. a
Fallah, B., Saberi, A. A., and Sodoudi, S.: Emergence of global scaling behaviour in the coupled Earth-atmosphere interaction, Sci. Rep., 6, 34005, https://doi.org/10.1038/srep34005, 2016a. a
Fallah, B., Sodoudi, S., and Cubasch, U.: Westerly jet stream and past millennium climate change in Arid Central Asia simulated by COSMO-CLM model, Theor. Appl. Climatol., 124, 1079–1088, 2016b. a
Fallah, B., Russo, E., Menz, C., Hoffmann, P., Didovets, I., and Hattermann, F. F.: Anthropogenic influence on extreme temperature and precipitation in Central Asia, Sci. Rep., 13, 6854, https://doi.org/10.1038/s41598-023-33921-6, 2023. a, b
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, 2017. a
Fotso-Nguemo, T. C., Vondou, D. A., Pokam, W. M., Djomou, Z. Y., Diallo, I., Haensler, A., Tchotchou, L. A. D., Kamsu-Tamo, P. H., Gaye, A. T., and Tchawoua, C.: On the added value of the regional climate model REMO in the assessment of climate change signal over Central Africa, Clim. Dynam., 49, 3813–3838, 2017. a
Fowler, H. J., Blenkinsop, S., and Tebaldi, C.: Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, Int. J. Climatol., 27, 1547–1578, 2007. a
Frei, C., Christensen, J. H., Déqué, M., Jacob, D., Jones, R. G., and Vidale, P. L.: Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps, J. Geophys. Res.-Atmos., 108, 4124, https://doi.org/10.1029/2002JD002287, 2003. a
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 1–21, 2015. a
Galea, D., Ma, H.-Y., Wu, W.-Y., and Kobayashi, D.: Deep Learning Image Segmentation for Atmospheric Rivers, Artificial Intelligence for the Earth Systems, 3, 230048, https://doi.org/10.1175/AIES-D-23-0048.1, 2024. a
Gao, Y., Xu, Y., Shi, Y., Giorgi, F., and Zhai, P.: Evaluating the Performance of RegCM4 over CORDEX East Asia against the Reanalysis Data, J. Climate, 28, 660–676, 2015. a
Gardoll, S. and Boucher, O.: Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset, Geosci. Model Dev., 15, 7051–7073, https://doi.org/10.5194/gmd-15-7051-2022, 2022. a
Giot, O., Termonia, P., Degrauwe, D., De Troch, R., Caluwaerts, S., Smet, G., Berckmans, J., Deckmyn, A., De Cruz, L., De Meutter, P., Duerinckx, A., Gerard, L., Hamdi, R., Van den Bergh, J., Van Ginderachter, M., and Van Schaeybroeck, B.: Validation of the ALARO-0 model within the EURO-CORDEX framework, Geosci. Model Dev., 9, 1143–1152, https://doi.org/10.5194/gmd-9-1143-2016, 2016. a
Harder, P.: Hard-Constrained Deep Learning for Climate Downscaling (v1.0.0), Zenodo [software], https://doi.org/10.5281/zenodo.8150694, 2023. a
Harder, P., Yang, Q., Ramesh, V., Sattigeri, P., Hernandez-Garcia, A., Watson, C., Szwarcman, D., and Rolnick, D.: Generating physically-consistent high-resolution climate data with hard-constrained neural networks, arXiv [preprint], arXiv:2208.05424, 18, 109–122, 2022.
Hodson, T. O.: Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not, Geosci. Model Dev., 15, 5481–5487, https://doi.org/10.5194/gmd-15-5481-2022, 2022. a
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, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021. a, b, c
Jacob, D. and Podzun, R.: Sensitivity studies with the regional climate model REMO, Meteorol. Atmos. Phys., 63, 119–129, 1997. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: New high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, 2014. a
Kendon, E., Roberts, N., Fowler, H., Roberts, M., Chan, S., and Senior, C.: Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Clim. Change, 4, 570–576, 2014. a
Kikstra, J. S., Nicholls, Z. R. J., Smith, C. J., Lewis, J., Lamboll, R. D., Byers, E., Sandstad, M., Meinshausen, M., Gidden, M. J., Rogelj, J., Kriegler, E., Peters, G. P., Fuglestvedt, J. S., Skeie, R. B., Samset, B. H., Wienpahl, L., van Vuuren, D. P., van der Wijst, K.-I., Al Khourdajie, A., Forster, P. M., Reisinger, A., Schaeffer, R., and Riahi, K.: The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures, Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, 2022. a
Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., and Lerner-Lam, A.: A global landslide catalog for hazard applications: method, results, and limitations, Natural Hazards, 52, 561–575, 2010. a
Kjellström, E., Nikulin, G., Hansson, U., Strandberg, G., Ullerstig, A., Willén, U., and Wyser, K.: 21st century changes in the European climate: uncertainties derived from an ensemble of regional climate model simulations, Tellus A, 63, 24–40, 2011. a
Klok, E. and Klein Tank, A.: Updated and extended European dataset of daily weather observations, Int. J. Climatol., 28, 2081–2095, 2008. a
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014. a
Kurth, T., Treichler, S., Romero, J., Mudigonda, M., Luehr, N., Phillips, E., Mahesh, A., Matheson, M., Deslippe, J., Fatica, M., Prabhat, and Houston, M.: Exascale deep learning for climate analytics, in: SC18: International conference for high performance computing, networking, storage and analysis, 11–16 November 2018, Dallas,Texas, USA, 649–660, IEEE, 2018. a
Laflamme, E. M., Linder, E., and Pan, Y.: Statistical downscaling of regional climate model output to achieve projections of precipitation extremes, Weather and Climate Extremes, 12, 15–23, 2016. a
Li, L., Bisht, G., and Leung, L. R.: Spatial heterogeneity effects on land surface modeling of water and energy partitioning, Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, 2022. a
Maraun, D. and Widmann, M.: Statistical downscaling and bias correction for climate research, Cambridge University Press, ISBN 9781107066052, https://doi.org/10.1017/9781107588783, 2018. a
Maraun, D., Widmann, M., Gutiérrez, J. M., Kotlarski, S., Chandler, R. E., Hertig, E., Wibig, J., Huth, R., and Wilcke, R. A.: VALUE: A framework to validate downscaling approaches for climate change studies, Earth's Future, 3, 1–14, 2015. a
Meredith, E. P., Rust, H. W., and Ulbrich, U.: A classification algorithm for selective dynamical downscaling of precipitation extremes, Hydrol. Earth Syst. Sci., 22, 4183–4200, https://doi.org/10.5194/hess-22-4183-2018, 2018. a
Muttaqien, F. H., Rahadianti, L., and Latifah, A. L.: Downscaling for Climate Data in Indonesia Using Image-to-Image Translation Approach, in: 2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 23–25 October 2021, Depok, Indonesia, 1–8, IEEE, 2021. a
Naddaf, M.: Climate change is costing trillions-and low-income countries are paying the price, Nature, https://doi.org/10.1038/d41586-022-03573-z, 2022. a
Panitz, H.-J., Dosio, A., Büchner, M., Lüthi, D., and Keuler, K.: COSMO-CLM (CCLM) climate simulations over CORDEX-Africa domain: analysis of the ERA-Interim driven simulations at 0.44 and 0.22 resolution, Clim. Dynam., 42, 3015–3038, 2014. a
Qiu, Y., Feng, J., Yan, Z., and Wang, J.: HCPD-CA: high-resolution climate projection dataset in central Asia, Earth Syst. Sci. Data, 14, 2195–2208, https://doi.org/10.5194/essd-14-2195-2022, 2022. a
Racah, E., Beckham, C., Maharaj, T., Ebrahimi Kahou, S., Prabhat, M., and Pal, C.: Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events, Adv. Neur. In., 30, 3403–3414, https://doi.org/10.5555/3295222.3295380, 2017. a
Rai, P., Bangelesa, F., Abel, D., Ziegler, K., Huang, J., Schaffhauser, T., Pollinger, F., Disse, M., and Paeth, H.: Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE, Theor. Appl. Climatol., 155, 6015–6039, https://doi.org/10.1007/s00704-024-04976-w, 2024. a
Rajczak, J., Pall, P., and Schär, C.: Projections of extreme precipitation events in regional climate simulations for Europe and the Alpine Region, J. Geophys. Res.-Atmos., 118, 3610–3626, 2013. a
Rampal, N., Hobeichi, S., Gibson, P. B., Baño-Medina, J., Abramowitz, G., Beucler, T., González-Abad, J., Chapman, W., Harder, P., and Gutiérrez, J. M.: Enhancing Regional Climate Downscaling through Advances in Machine Learning, Artificial Intelligence for the Earth Systems, 3, 230066, https://doi.org/10.1175/AIES-D-23-0066.1, 2024. a
Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., Stouffer, R. J., Sumi, A., Taylor, K. E., AchutaRao, K., Allan, R., Berger, A., Blatter, H., Bonfils, C., Boone, A., Bretherton, C., Broccoli, A., Brovkin, V., Cai, W., Claussen, M., Dirmeyer, P., Doutriaux, C., Drange, H., Dufresne, J.-L., Emori, S., Forster, P., Frei, A., Ganopolski, A., Gent, P., Gleckler, P., Goosse, H., Graham, R., Gregory, J., Gudgel, R., Hall, A., Hallegatte, S., Hasumi, H., Henderson-Sellers, A., Hendon, H., Hodges, K., Holland, M., Holtslag, A., Hunke, E., Huybrechts, P., Ingram, W., Joos, F., Kirtman, B., Klein, S., Koster, R., Kushner, P., Lanzante, J., Latif, M., Lau, N.-C., Meinshausen, M., Monahan, A., Murphy, J., Osborn, T., Pavlova, T., Petoukhov, V., Phillips, T., Power, S., Rahmstorf, S., Raper, S., Renssen, H., Rind, D., Roberts, M., Rosati, A., Schär, C., Schmittner, A., Scinocca, J., Seidov, D., Slater, A., Slingo, J., Smith, D., Soden, B., Stern, W., Stone, D., Sudo, K., Takemura, T., Tselioudis, G., Webb, M., and Wild, M.: Climate models and their evaluation, in: Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR), edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., 589–662, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://www.ipcc.ch/site/assets/uploads/2018/02/ar4-wg1-chapter8-1.pdf (last access: 14 January 2025), 2007. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat, f.: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, 2019. a
Reyer, C. P. O., Otto, I. M., Adams, S., Albrecht, T., Baarsch, F., Cartsburg, M., Coumou, D., Eden, A., Ludi, E., Marcus, R., Mengel, M., Mosello, B., Robinson, A., Schleussner, C.-F., Serdeczny, O., and Stagl, J.: Climate change impacts in Central Asia and their implications for development, Reg. Environ. Change, 17, 1639–1650, 2017. a, b
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.: The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview, Global Environ. Change, 42, 153–168, 2017. a
Rockel, B. and Geyer, B.: The performance of the regional climate model CLM in different climate regions, as simulated in a transient climate change experiment, Clim. Dynam., 31, 713–728, 2008. a
Rummukainen, M.: State-of-the-art with regional climate models, Wiley Interdisciplinary Reviews: Climate Change, 1, 82–96, 2010. a
Russo, E., Fallah, B., Ludwig, P., Karremann, M., and Raible, C. C.: The long-standing dilemma of European summer temperatures at the mid-Holocene and other considerations on learning from the past for the future using a regional climate model, Clim. Past, 18, 895–909, https://doi.org/10.5194/cp-18-895-2022, 2022. a
Sørland, S. L., Brogli, R., Pothapakula, P. K., Russo, E., Van de Walle, J., Ahrens, B., Anders, I., Bucchignani, E., Davin, E. L., Demory, M.-E., Dosio, A., Feldmann, H., Früh, B., Geyer, B., Keuler, K., Lee, D., Li, D., van Lipzig, N. P. M., Min, S.-K., Panitz, H.-J., Rockel, B., Schär, C., Steger, C., and Thiery, W.: COSMO-CLM regional climate simulations in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework: a review, Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, 2021. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012. a
Volosciuk, C., Maraun, D., Vrac, M., and Widmann, M.: A combined statistical bias correction and stochastic downscaling method for precipitation, Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, 2017. a
Wang, X., Otto, M., and Scherer, D.: Atmospheric triggering conditions and climatic disposition of landslides in Kyrgyzstan and Tajikistan at the beginning of the 21st century, Nat. Hazards Earth Syst. Sci., 21, 2125–2144, https://doi.org/10.5194/nhess-21-2125-2021, 2021. a
Watson-Parris, D., Rao, Y., Olivié, D., Seland, O., Nowack, P., Camps-Valls, G., Stier, P., Bouabid, S., Dewey, M., Fons, E., Gonzalez, J., Harder, P., Jeggle, K., Lenhardt, J., Manshausen, P., Novitasari, M., Ricard, L., and Roesch, C.: ClimateBench v1. 0: A benchmark for data-driven climate projections, J. Adv. Model. Earth Sy., 14, e2021MS002954, https://doi.org/10.1029/2021MS002954, 2022. a
Xu, P., Wang, L., and Ming, J.: Central Asian precipitation extremes affected by an intraseasonal planetary wave pattern, J. Climate, 35, 2603–2616, 2022. a
Xu, Z., Han, Y., Tam, C.-Y., Yang, Z.-L., and Fu, C.: Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100), Sci. Data, 8, 293, https://doi.org/10.1038/s41597-021-01079-3, 2021. a
Yan, Y., You, Q., Wu, F., Pepin, N., and Kang, S.: Surface mean temperature from the observational stations and multiple reanalyses over the Tibetan Plateau, Clim. Dynam., 55, 2405–2419, 2020. a
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., and Fraundorfer, F.: Deep learning in remote sensing: A comprehensive review and list of resources, IEEE Geoscience and Remote Sensing Magazine, 5, 8–36, 2017. a
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
We tried to contribute to a local climate change impact study in central Asia, a region that is...
