Articles | Volume 14, issue 1
https://doi.org/10.5194/esd-14-81-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-81-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
26 Jan 2023
Research article | Highlight paper |
| 26 Jan 2023
| 26 Jan 2023
Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change
Iris Elisabeth de Vries
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Sebastian Sippel
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Angeline Greene Pendergrass
Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA
National Center for Atmospheric Research, Boulder, CO, USA
Reto Knutti
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Related authors
No articles found.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025, https://doi.org/10.5194/gmd-18-4399-2025, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone and greenhouse gases alone are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies and to underpin the next IPCC report.
Peter Pfleiderer, Anna Merrifield, István Dunkl, Homer Durand, Enora Cariou, Julien Cattiaux, and Sebastian Sippel
EGUsphere, https://doi.org/10.5194/egusphere-2025-2397, https://doi.org/10.5194/egusphere-2025-2397, 2025
Short summary
Short summary
Due to changes in atmospheric circulation some regions are warming quicker than others. Statistical methods are used to estimate how much of the local summer temperature trends are due to circulation changes. We evaluate these methods by comparing their estimates to special simulations representing only temperature changes related to circulation changes. By applying the methods to observations of 1979–2023 we find that half of the warming over parts of Europe is related to circulation changes.
Na Li, Sebastian Sippel, Nora Linscheid, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
EGUsphere, https://doi.org/10.5194/egusphere-2025-1924, https://doi.org/10.5194/egusphere-2025-1924, 2025
Short summary
Short summary
The global land carbon sink has increased since the pre-industrial period, mainly caused by increasing atmospheric CO2 emissions and climate change. However, the large year-to-year variations can mask or amplify this trend. Here, we detect the time for the anthropogenic signal to emerge over natural variations in land carbon sink. We removed the circulation-induced variations in the global land carbon sink and effectively reduced the detection time of anthropogenic signal.
Luna Bloin-Wibe, Robin Noyelle, Vincent Humphrey, Urs Beyerle, Reto Knutti, and Erich Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2025-525, https://doi.org/10.5194/egusphere-2025-525, 2025
Short summary
Short summary
Weather extremes have become more frequent due to climate change. It is therefore crucial to understand them, but since they are rarer than average weather, they are challenging to study. Ensemble Boosting (EB) is a tool that generates extreme climate model events efficiently, but without directly estimating their probability. Here, we present a method to recover these probabilities for a global climate model. EB can thus now be used to find extremes with meaningful statistical information.
Detlef van Vuuren, Brian O'Neill, Claudia Tebaldi, Louise Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Benjamin Sanderson, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh Fall, Katja Frieler, Matthew Gidden, Laila Gohar, Andrew Jones, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camila Mathison, Shahbaz Mehmood, Luciana Prado, Qiang Zhang, Steven Rose, Alexander Ruane, Carl-Friederich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha Vishwanathan, Tokuta Yokohata, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3765, https://doi.org/10.5194/egusphere-2024-3765, 2025
Short summary
Short summary
We propose a set of six plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
Short summary
Short summary
We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
Short summary
Short summary
The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Sebastian Sippel, Clair Barnes, Camille Cadiou, Erich Fischer, Sarah Kew, Marlene Kretschmer, Sjoukje Philip, Theodore G. Shepherd, Jitendra Singh, Robert Vautard, and Pascal Yiou
Weather Clim. Dynam., 5, 943–957, https://doi.org/10.5194/wcd-5-943-2024, https://doi.org/10.5194/wcd-5-943-2024, 2024
Short summary
Short summary
Winter temperatures in central Europe have increased. But cold winters can still cause problems for energy systems, infrastructure, or human health. Here we tested whether a record-cold winter, such as the one observed in 1963 over central Europe, could still occur despite climate change. The answer is yes: it is possible, but it is very unlikely. Our results rely on climate model simulations and statistical rare event analysis. In conclusion, society must be prepared for such cold winters.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
Short summary
Short summary
We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Na Li, Sebastian Sippel, Alexander J. Winkler, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
Earth Syst. Dynam., 13, 1505–1533, https://doi.org/10.5194/esd-13-1505-2022, https://doi.org/10.5194/esd-13-1505-2022, 2022
Short summary
Short summary
Quantifying the imprint of large-scale atmospheric circulation dynamics and associated carbon cycle responses is key to improving our understanding of carbon cycle dynamics. Using a statistical model that relies on spatiotemporal sea level pressure as a proxy for large-scale atmospheric circulation, we quantify the fraction of interannual variability in atmospheric CO2 growth rate and the land CO2 sink that are driven by atmospheric circulation variability.
Benjamin M. Sanderson, Angeline G. Pendergrass, Charles D. Koven, Florent Brient, Ben B. B. Booth, Rosie A. Fisher, and Reto Knutti
Earth Syst. Dynam., 12, 899–918, https://doi.org/10.5194/esd-12-899-2021, https://doi.org/10.5194/esd-12-899-2021, 2021
Short summary
Short summary
Emergent constraints promise a pathway to the reduction in climate projection uncertainties by exploiting ensemble relationships between observable quantities and unknown climate response parameters. This study considers the robustness of these relationships in light of biases and common simplifications that may be present in the original ensemble of climate simulations. We propose a classification scheme for constraints and a number of practical case studies.
Christina Heinze-Deml, Sebastian Sippel, Angeline G. Pendergrass, Flavio Lehner, and Nicolai Meinshausen
Geosci. Model Dev., 14, 4977–4999, https://doi.org/10.5194/gmd-14-4977-2021, https://doi.org/10.5194/gmd-14-4977-2021, 2021
Short summary
Short summary
Quantifying dynamical and thermodynamical components of regional precipitation change is a key challenge in climate science. We introduce a novel statistical model (Latent Linear Adjustment Autoencoder) that combines the flexibility of deep neural networks with the robustness advantages of linear regression. The method enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
Short summary
Short summary
We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Milan Flach, Alexander Brenning, Fabian Gans, Markus Reichstein, Sebastian Sippel, and Miguel D. Mahecha
Biogeosciences, 18, 39–53, https://doi.org/10.5194/bg-18-39-2021, https://doi.org/10.5194/bg-18-39-2021, 2021
Short summary
Short summary
Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. We study the impact of droughts and heatwaves on the uptake of CO2 of different vegetation types at the global scale. We find that agricultural areas are generally strongly affected. Forests instead are not particularly sensitive to the events under scrutiny. This implies different water management strategies of forests but also a lack of sensitivity to remote-sensing-derived vegetation activity.
Lukas Brunner, Angeline G. Pendergrass, Flavio Lehner, Anna L. Merrifield, Ruth Lorenz, and Reto Knutti
Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, https://doi.org/10.5194/esd-11-995-2020, 2020
Short summary
Short summary
In this study, we weight climate models by their performance with respect to simulating aspects of historical climate and their degree of interdependence. Our method is found to increase projection skill and to correct for structurally similar models. The weighted end-of-century mean warming (2081–2100 relative to 1995–2014) is 3.7 °C with a likely (66 %) range of 3.1 to 4.6 °C for the strong climate change scenario SSP5-8.5; this is a reduction of 0.4 °C compared with the unweighted mean.
Anna Louise Merrifield, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto Knutti
Earth Syst. Dynam., 11, 807–834, https://doi.org/10.5194/esd-11-807-2020, https://doi.org/10.5194/esd-11-807-2020, 2020
Short summary
Short summary
Justifiable uncertainty estimates of future change in northern European winter and Mediterranean summer temperature can be obtained by weighting a multi-model ensemble comprised of projections from different climate models and multiple projections from the same climate model. Weights reduce the influence of model biases and handle dependence by identifying a projection's model of origin from historical characteristics; contributions from the same model are scaled by the number of members.
Cited articles
Allan, R. P., Liu, C., Zahn, M., Lavers, D. A., Koukouvagias, E., and
Bodas-Salcedo, A.: Physically consistent responses of the global atmospheric
hydrological cycle in models and observations, Surv. Geophys., 35,
533–552, https://doi.org/10.1007/s10712-012-9213-z, 2014. a
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and
the hydrologic cycle, Nature, 419, 228–232, https://doi.org/10.1038/nature01092, 2002. a
Allen, M. R. and Stott, P. A.: Estimating the signal amplitudes in optimal
fingerprinting, Part I: Theory, Clim. Dynam., 21, 477–491, 2003. a
Avila, F. B., Dong, S., Menang, K. P., Rajczak, J., Renom, M., Donat, M. G.,
and Alexander, L. V.: Systematic investigation of gridding-related scaling
effects on annual statistics of daily temperature and precipitation maxima: A
case study for south-east Australia, Weather and Climate Extremes, 9, 6–16,
https://doi.org/10.1016/j.wace.2015.06.003, 2015. a
Balan Sarojini, B., Stott, P. A., Black, E., and Polson, D.: Fingerprints of
changes in annual and seasonal precipitation from CMIP5 models over land and
ocean, Geophys. Res. Lett., 39, L21706, https://doi.org/10.1029/2012GL053373, 2012. a, b
Balan Sarojini, B., Stott, P. A., and Black, E.: Detection and attribution of
human influence on regional precipitation, Nat. Clim. Change, 6,
669–675, https://doi.org/10.1038/nclimate2976, 2016. a
Barnes, E. A., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson,
D.: Viewing forced climate patterns through an AI Lens, Geophys. Res.
Lett., 46, 13389–13398, https://doi.org/10.1029/2019GL084944, 2019. a, b, c
Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and
Anderson, D.: Indicator Patterns of Forced Change Learned by an Artificial
Neural Network, J. Adv. Model. Earth Sy., 12,
e2020MS002195, https://doi.org/10.1029/2020MS002195, 2020. a, b
Bonfils, C. J., Santer, B. D., Fyfe, J. C., Marvel, K., Phillips, T. J., and
Zimmerman, S. R.: Human influence on joint changes in temperature, rainfall
and continental aridity, Nat. Clim. Change, 10, 726–731,
https://doi.org/10.1038/s41558-020-0821-1, 2020. a, b, c
Borodina, A., Fischer, E. M., and Knutti, R.: Models are likely to
underestimate increase in heavy rainfall in the extratropical regions with
high rainfall intensity, Geophys. Res. Lett., 44, 7401–7409,
https://doi.org/10.1002/2017GL074530, 2017.
a, b
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1532, 2019a. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5251, 2019b. a
Byrne, M. P. and O'Gorman, P. A.: The Response of Precipitation Minus
Evapotranspiration to Climate Warming: Why the “Wet-Get-Wetter,
Dry-Get-Drier” Scaling Does Not Hold over Land, J. Climate, 28,
8078–8092, https://doi.org/10.1175/JCLI-D-15-0369.1, 2015. a, b
Byun, Y.-H., Lim, Y.-J., Shim, S., Sung, H. M., Sun, M., Kim, J., Kim, B.-H., Lee, J.-H., and Moon, H.: NIMS-KMA KACE1.0-G model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.2242, 2019a. a
Byun, Y.-H., Lim, Y.-J., Sung, H. M., Kim, J., Sun, M., and Kim, B.-H.: NIMS-KMA KACE1.0-G model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8425, 2019b. a
Chen, D., Rojas, M., Samset, B., Cobb, K., Niang, A. D., Edwards, P., Emori,
S., Faria, S., Hawkins, E., Hope, P., Huybrechts, P., Meinshausen, M.,
Mustafa, S., Plattner, G.-K., and Tréguier, A.-M.: Framing, Context, and
Methods, in: 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.,
chap. 1, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 2021. a
Contractor, S., Donat, M. G., Alexander, L. V., Ziese, M., Meyer-Christoffer, A., Schneider, U., Rustemeier, E., Becker, A., Durre, I., and Vose, R. S.: Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016, Hydrol. Earth Syst. Sci., 24, 919–943, https://doi.org/10.5194/hess-24-919-2020, 2020. a
Cowtan, K. and Way, R. G.: Coverage bias in the HadCRUT4 temperature series and
its impact on recent temperature trends, Q. J. Roy.
Meteor. Soc., 140, 1935–1944, https://doi.org/10.1002/qj.2297, 2014. a
Dai, A.: Recent Climatology, Variability, and Trends in Global Surface
Humidity, J. Climate, 19, 3589–3606, https://doi.org/10.1175/JCLI3816.1,
2006. a
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate
change projections: the role of internal variability, Clim. Dynam., 38,
527–546, https://doi.org/10.1007/s00382-010-0977-x, 2012. a
de Vries, I. E.: Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change – code and data, ETH Zurich [code, data set], https://doi.org/10.3929/ethz-b-000589377, 2023. a
Dix, M., Bi, D., Dobrohotoff, P., Fiedler, R., Harman, I., Law, R., Mackallah, C., Marsland, S., O'Farrell, S., Rashid, H., Srbinovsky, J., Sullivan, A., Trenham, C., Vohralik, P., Watterson, I., Williams, G., Woodhouse, M., Bodman, R., Dias, F. B., Domingues, C. M., Hannah, N., Heerdegen, A., Savita, A., Wales, S., Allen, C., Druken, K., Evans, B., Richards, C., Ridzwan, S. M., Roberts, D., Smillie, J., Snow, K., Ward, M., and Yang, R.: CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.2285, 2019a. a
Dix, M., Bi, D., Dobrohotoff, P., Fiedler, R., Harman, I., Law, R., Mackallah, C., Marsland, S., O'Farrell, S., Rashid, H., Srbinovsky, J., Sullivan, A., Trenham, C., Vohralik, P., Watterson, I., Williams, G., Woodhouse, M., Bodman, R., Dias, F. B., Domingues, C. M., Hannah, N., Heerdegen, A., Savita, A., Wales, S., Allen, C., Druken, K., Evans, B., Richards, C., Ridzwan, S. M., Roberts, D., Smillie, J., Snow, K., Ward, M., and Yang, R.: CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4311, 2019b. a
Donat, M., Alexander, L., Yang, H., Durre, I., Vose, R., and Caesar, J.: Global
Land-Based Datasets for Monitoring Climatic Extremes, B.
Am. Meteorol. Soc., 94, 997–1006,
https://doi.org/10.1175/BAMS-D-12-00109.1, 2013 (data available at: https://www.climdex.org/access/, last access: April 2021). a, b, c, d
Douville, H., Raghavan, K., Renwick, J., Allan, R. P., Arias, P. A., Barlow,
M., Cerezo-Mota, R., Cherchi, A., Gan, T. Y., Gergis, J., Jiang, D., Khan,
A., Mba, W. P., Rosenfeld, D., Tierney, J., and Zolina, O.: Water Cycle
Changes, in: 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.,
chap. 8, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 2021. a, b, c, d, e
Dunn, R. J. H., Alexander, L. V., Donat, M. G., Zhang, X., Bador, M., Herold,
N., Lippmann, T., Allan, R., Aguilar, E., Barry, A. A., Brunet, M., Caesar,
J., Chagnaud, G., Cheng, V., Cinco, T., Durre, I., de Guzman, R., Htay,
T. M., Wan Ibadullah, W. M., Bin Ibrahim, M. K. I., Khoshkam, M., Kruger, A.,
Kubota, H., Leng, T. W., Lim, G., Li-Sha, L., Marengo, J., Mbatha, S.,
McGree, S., Menne, M., de los Milagros Skansi, M., Ngwenya, S., Nkrumah, F.,
Oonariya, C., Pabon-Caicedo, J. D., Panthou, G., Pham, C., Rahimzadeh, F.,
Ramos, A., Salgado, E., Salinger, J., Sané, Y., Sopaheluwakan, A.,
Srivastava, A., Sun, Y., Timbal, B., Trachow, N., Trewin, B., van der
Schrier, G., Vazquez-Aguirre, J., Vasquez, R., Villarroel, C., Vincent, L.,
Vischel, T., Vose, R., and Bin Hj Yussof, M. N.: Development of an Updated
Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes:
HadEX3, J. Geophys. Res.-Atmos., 125, e2019JD032263,
https://doi.org/10.1029/2019JD032263, 2020 (data available at: https://www.climdex.org/access/, last access: April 2021). a, b, c, d, e, f, g
EC-Earth Consortium (EC-Earth): EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.251, 2019a. a
EC-Earth Consortium (EC-Earth): EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.727, 2019b. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fischer, E. M. and Knutti, R.: Observed heavy precipitation increase confirms
theory and early models, Nat. Clim. Change, 6, 986–991,
https://doi.org/10.1038/nclimate3110, 2016. a, b, c
Fischer, E. M., Sedláček, J., Hawkins, E., and Knutti, R.: Models agree on
forced response pattern of precipitation and temperature extremes,
Geophys. Res. Lett., 41, 8554–8562, https://doi.org/10.1002/2014GL062018,
2014. a
Fläschner, D., Mauritsen, T., and Stevens, B.: Understanding the Intermodel
Spread in Global-Mean Hydrological Sensitivity, J. Climate, 29, 801–817, https://doi.org/10.1175/JCLI-D-15-0351.1, 2016. a
Friedman, J. H., Hastie, T., and Tibshirani, R.: Regularization Paths for
Generalized Linear Models via Coordinate Descent, J. Stat.
Softw., 33, 1–22, https://doi.org/10.18637/jss.v033.i01, 2010. a, b, c, d
Giorgi, F., Coppola, E., and Raffaele, F.: A consistent picture of the
hydroclimatic response to global warming from multiple indices: Models and
observations, J. Geophys. Res.-Atmos., 119,
11695–11708, https://doi.org/10.1002/2014JD022238, 2014. a
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Kuhlbrodt, T., and Walton, J.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1567, 2019. a
Hajima, T., Abe, M., Arakawa, O., Suzuki, T., Komuro, Y., Ogura, T., Ogochi, K., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Ohgaito, R., Ito, A., Yamazaki, D., Ito, A., Takata, K., Watanabe, S., Kawamiya, M., and Tachiiri, K.: MIROC MIROC-ES2L model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5710, 2019. a
Hasselmann, K.: On the signal-to-noise problem in atmospheric response studies,
in: Meteorology over the tropical oceans, edited by: Shaw, D. B.,
251–259, Royal Meteorological Society, https://hdl.handle.net/21.11116/0000-0003-12C1-E (last access: 23 January 2023), 1979. a
Hawkins, E. and Sutton, R.: Time of emergence of climate signals, Geophys.
Res. Lett., 39, L01702, https://doi.org/10.1029/2011GL050087, 2012. a
Hawkins, E., Frame, D., Harrington, L., Joshi, M., King, A., Rojas, M., and
Sutton, R.: Observed Emergence of the Climate Change Signal: From the
Familiar to the Unknown, Geophys. Res. Lett., 47, e2019GL086259,
https://doi.org/10.1029/2019GL086259, 2020. a, b, c
Hegerl, G. C., von Storch, H., Hasselmann, K., Santer, B. D., Cubasch, U., and
Jones, P. D.: Detecting Greenhouse-Gas-Induced Climate Change with an Optimal
Fingerprint Method, J. Climate, 9, 2281–2306,
https://doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2, 1996. a, b
Held, I. M. and Soden, B. J.: Robust Responses of the Hydrological Cycle to
Global Warming, J. Climate, 19, 5686–5699,
https://doi.org/10.1175/JCLI3990.1, 2006. a
Hoerling, M., Eischeid, J., and Perlwitz, J.: Regional Precipitation Trends:
Distinguishing Natural Variability from Anthropogenic Forcing, J.
Climate, 23, 2131–2145, https://doi.org/10.1175/2009JCLI3420.1, 2010. a
Kent, C., Chadwick, R., and Rowell, D. P.: Understanding Uncertainties in
Future Projections of Seasonal Tropical Precipitation, J. Climate,
28, 4390–4413, https://doi.org/10.1175/JCLI-D-14-00613.1, 2015. a, b
King, A. D., Donat, M. G., Fischer, E. M., Hawkins, E., Alexander, L. V.,
Karoly, D. J., Dittus, A. J., Lewis, S. C., and Perkins, S. E.: The timing of
anthropogenic emergence in simulated climate extremes, Environ. Res.
Lett., 10, 094015, https://doi.org/10.1088/1748-9326/10/9/094015, 2015. a
Kirchmeier-Young, M. C. and Zhang, X.: Human influence has intensified extreme
precipitation in North America, P. Natl. Acad.
Sci. USA, 117, 13308–13313, https://doi.org/10.1073/pnas.1921628117, 2020. a
Knutson, T. R. and Zeng, F.: Model Assessment of Observed Precipitation Trends
over Land Regions: Detectable Human Influences and Possible Low Bias in Model
Trends, J. Climate, 31, 4617–4637, https://doi.org/10.1175/JCLI-D-17-0672.1,
2018. a, b
Kotz, M., Wenz, L., Lange, S., and Levermann, A.: Changes in mean and extreme
precipitation scale universally with global mean temperature across and
within climate models, EarthArXiv [preprint], https://doi.org/10.31223/X5C631, 2022. a, b
Labe, Z. M. and Barnes, E. A.: Detecting Climate Signals Using Explainable AI
With Single-Forcing Large Ensembles, J. Adv. Model. Earth
Sy., 13, e2021MS002464, https://doi.org/10.1029/2021MS002464,
2021. a
Li, C., Zwiers, F., Zhang, X., Li, G., Sun, Y., and Wehner, M.: Changes in
Annual Extremes of Daily Temperature and Precipitation in CMIP6 Models,
J. Climate, 34, 3441–3460, https://doi.org/10.1175/JCLI-D-19-1013.1, 2021. a
Li, L.: CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.2056, 2019. a
Madakumbura, G. D., Thackeray, C. W., Norris, J., Goldenson, N., and Hall, A.:
Anthropogenic influence on extreme precipitation over global land areas seen
in multiple observational datasets, Nat. Commun., 12, 3944,
https://doi.org/10.1038/s41467-021-24262-x, 2021. a, b
Marvel, K. and Bonfils, C.: Identifying external influences on global
precipitation, P. Natl. Acad. Sci. USA, 110,
19301–19306, https://doi.org/10.1073/pnas.1314382110, 2013. a, b, c, d
Mehran, A., AghaKouchak, A., and Phillips, T. J.: Evaluation of CMIP5
continental precipitation simulations relative to satellite-based
gauge-adjusted observations, J. Geophys. Res.-Atmos.,
119, 1695–1707, https://doi.org/10.1002/2013JD021152, 2014. a
Min, S.-K., Zhang, X., Zwiers, F. W., and Hegerl, G. C.: Human contribution to
more-intense precipitation extremes, Nature, 470, 378–381,
https://doi.org/10.1038/nature09763, 2011. a, b
Noake, K., Polson, D., Hegerl, G., and Zhang, X.: Changes in seasonal land
precipitation during the latter twentieth-century, Geophys. Res.
Lett., 39, L03706, https://doi.org/10.1029/2011GL050405, 2012. a, b
O'Gorman, P. A. and Schneider, T.: The physical basis for
increases in precipitation extremes in simulations of 21st-century climate
change, P. Natl. Acad. Sci. USA, 106,
14773–14777, https://doi.org/10.1073/pnas.0907610106, 2009. a, b
Paik, S., Min, S.-K., Zhang, X., Donat, M. G., King, A. D., and Sun, Q.:
Determining the Anthropogenic Greenhouse Gas Contribution to the Observed
Intensification of Extreme Precipitation, Geophys. Res. Lett., 47,
e2019GL086875, https://doi.org/10.1029/2019GL086875, 2020. a, b
Pendergrass, A. G.: The Global-Mean Precipitation Response to CO2-Induced
Warming in CMIP6 Models, Geophys. Res. Lett., 47, e2020GL089964,
https://doi.org/10.1029/2020GL089964, 2020. a
Pendergrass, A. G. and Hartmann, D. L.: The atmospheric energy constraint on
global-mean precipitation change, J. Climate, 27, 757–768,
https://doi.org/10.1175/JCLI-D-13-00163.1, 2014. a
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C., and Sanderson, B. M.:
Precipitation variability increases in a warmer climate, Sci. Rep.-UK,
7, 1–9, https://doi.org/10.1038/s41598-017-17966-y, 2017. a
Pfahl, S. and Wernli, H.: Quantifying the Relevance of Cyclones for
Precipitation Extremes, J. Climate, 25, 6770–6780,
https://doi.org/10.1175/JCLI-D-11-00705.1, 2012. a
Pfahl, S., O'Gorman, P. A., and Fischer, E. M.: Understanding the regional
pattern of projected future changes in extreme precipitation, Nat. Clim.
Change, 7, 423–427, https://doi.org/10.1038/nclimate3287, 2017. a, b, c, d
Polson, D., Hegerl, G. C., Zhang, X., and Osborn, T. J.: Causes of Robust
Seasonal Land Precipitation Changes, J. Climate, 26, 6679–6697,
https://doi.org/10.1175/JCLI-D-12-00474.1, 2013. a, b
Ribes, A. and Terray, L.: Application of regularised optimal fingerprinting to
attribution. Part II: application to global near-surface temperature, Clim.
Dynam., 41, 2837–2853, 2013. a
Ribes, A., Planton, S., and Terray, L.: Application of regularised optimal
fingerprinting to attribution. Part I: method, properties and idealised
analysis, Clim. Dynam., 41, 2817–2836, 2013. a
Roderick, M. L., Sun, F., Lim, W. H., and Farquhar, G. D.: A general framework for understanding the response of the water cycle to global warming over land and ocean, Hydrol. Earth Syst. Sci., 18, 1575–1589, https://doi.org/10.5194/hess-18-1575-2014, 2014. a
Rustemeier, E., Becker, A., Finger, P., Schneider, U., and Ziese, M.: GPCC Climatology Version 2020 at 2.5∘: Monthly Land-Surface Precipitation Climatology for Every Month and the Total Year from Rain-Gauges built on GTS-based and Historical Data, DWD [data set], https://doi.org/10.5676/DWD_GPCC/CLIM_M_V2020_250, 2020. a
Salzmann, M.: Global warming without global mean precipitation increase?,
Sci. Adv., 2, e1501572, https://doi.org/10.1126/sciadv.1501572, 2016. a
Santer, B. D., Taylor, K. E., Wigley, T. M., Penner, J. E., Jones, P. D., and
Cubasch, U.: Towards the detection and attribution of an anthropogenic effect
on climate, Clim. Dynam., 12, 77–100, https://doi.org/10.1007/BF00223722, 1995. a, b
Santer, B. D., Painter, J. F., Bonfils, C., Mears, C. A., Solomon, S., Wigley,
T. M. L., Gleckler, P. J., Schmidt, G. A., Doutriaux, C., Gillett, N. P.,
Taylor, K. E., Thorne, P. W., and Wentz, F. J.: Human and natural influences
on the changing thermal structure of the atmosphere, P.
Natl. Acad. Sci. USA, 110, 17235–17240,
https://doi.org/10.1073/pnas.1305332110, 2013. a, b
Schneider, U., Finger, P., Meyer-Christoffer, A., Rustemeier, E., Ziese, M.,
and Becker, A.: Evaluating the Hydrological Cycle over Land Using the
Newly-Corrected Precipitation Climatology from the Global Precipitation
Climatology Centre (GPCC), Atmosphere, 8, 52, https://doi.org/10.3390/atmos8030052, 2017. a, b, c
Seland, O., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.604, 2019a. a
Seland, Ø., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8217, 2019b. a
Shiogama, H., Abe, M., and Tatebe, H.: MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.898, 2019. a
Simon, N., Friedman, J. H., Hastie, T., and Tibshirani, R.: Regularization
Paths for Cox's Proportional Hazards Model via Coordinate Descent, J. Stat. Softw., 39, 1–13, https://doi.org/10.18637/jss.v039.i05, 2011. a, b, c, d
Sippel, S., Meinshausen, N., Fischer, E. M., Székely, E., and Knutti, R.:
Climate change now detectable from any single day of weather at global scale,
Nat. Clim. Change, 10, 35–41, https://doi.org/10.1038/s41558-019-0666-7, 2020. a, b, c
Sippel, S., Meinshausen, N., Székely, E., Fischer, E., Pendergrass, A. G.,
Lehner, F., and Knutti, R.: Robust detection of forced warming in the
presence of potentially large climate variability, Sci. Adv., 7,
eabh4429, https://doi.org/10.1126/sciadv.abh4429, 2021. a
Sun, Q., Zwiers, F., Zhang, X., and Yan, J.: Quantifying the Human Influence on
the Intensity of Extreme 1- and 5-Day Precipitation Amounts at Global,
Continental, and Regional Scales, J. Climate, 35, 195–210,
https://doi.org/10.1175/JCLI-D-21-0028.1, 2022. a, b, c
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1317, 2019a. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3673, 2019b. a
Tachiiri, K., Abe, M., Hajima, T., Arakawa, O., Suzuki, T., Komuro, Y., Ogochi, K., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Ohgaito, R., Ito, A., Yamazaki, D., Ito, A., Takata, K., Watanabe, S., and Kawamiya, M.: MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.936, 2019. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6298, 2019. a
Tatebe, H. and Watanabe, M.: MIROC MIROC6 model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5711, 2019. a
Tramblay, Y., Mimeau, L., Neppel, L., Vinet, F., and Sauquet, E.: Detection and attribution of flood trends in Mediterranean basins, Hydrol. Earth Syst. Sci., 23, 4419–4431, https://doi.org/10.5194/hess-23-4419-2019, 2019. a
Wehner, M., Gleckler, P., and Lee, J.: Characterization of long period return
values of extreme daily temperature and precipitation in the CMIP6 models:
Part 1, model evaluation, Weather and Climate Extremes, 30, 100283,
https://doi.org/10.1016/j.wace.2020.100283, 2020. a
Westra, S., Alexander, L. V., and Zwiers, F. W.: Global Increasing Trends in
Annual Maximum Daily Precipitation, J. Climate, 26, 3904–3918,
https://doi.org/10.1175/JCLI-D-12-00502.1, 2013. a
Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.793, 2019a. a
Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6675, 2019b. a
Wu, P., Christidis, N., and Stott, P.: Anthropogenic impact on Earth’s
hydrological cycle, Nat. Clim. Change, 3, 807–810,
https://doi.org/10.1038/nclimate1932, 2013. a, b, c
Zhang, X., Wan, H., Zwiers, F. W., Hegerl, G. C., and Min, S.-K.: Attributing
intensification of precipitation extremes to human influence, Geophys.
Res. Lett., 40, 5252–5257, https://doi.org/10.1002/grl.51010, 2013. a, b
Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., Wang, Y., Dobrohotoff, P., Srbinovsky, J., Stevens, L., Vohralik, P., Mackallah, C., Sullivan, A., O'Farrell, S., and Druken, K.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.2291, 2019a.
a
Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., Wang, Y., Dobrohotoff, P., Srbinovsky, J., Stevens, L., Vohralik, P., Mackallah, C., Sullivan, A., O'Farrell, S., and Druken, K.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4312, 2019b. a
Zittis, G., Bruggeman, A., and Lelieveld, J.: Revisiting future extreme
precipitation trends in the Mediterranean, Weather and Climate Extremes, 34,
100380, https://doi.org/10.1016/j.wace.2021.100380, 2021. a
Chief editor
Detecting and attributing forced precipitation changes is a long-standing challenge in climate science. This study proposes an approach to efficiently extract information on forced precipitation changes from climate data and models, which can be valuable both from a scientific and policy-making perspective.
Detecting and attributing forced precipitation changes is a long-standing challenge in climate...
Short summary
Precipitation change is an important consequence of climate change, but it is hard to detect and quantify. Our intuitive method yields robust and interpretable detection of forced precipitation change in three observational datasets for global mean and extreme precipitation, but the different observational datasets show different magnitudes of forced change. Assessment and reduction of uncertainties surrounding forced precipitation change are important for future projections and adaptation.
Precipitation change is an important consequence of climate change, but it is hard to detect and...
Altmetrics
Final-revised paper
Preprint