References
Addison, Henry J., Elizabeth J. Kendon, Suman Ravuri, Laurence Aitchison, and Peter A. G. Watson. 2026. “Machine Learning Emulation of Precipitation from Km-Scale UK Regional Climate Simulations Using a Diffusion Model.” Journal of Advances in Modeling Earth Systems 18 (3): e2025MS005140. https://doi.org/10.1029/2025MS005140.
Addison, Henry, Elizabeth Kendon, Suman Ravuri, Laurence Aitchison, and Peter A. G. Watson. 2022. “Machine Learning Emulation of a Local-Scale UK Climate Model.” Tackling Climate Change with Machine Learning Workshop, NeurIPS 2022. https://arxiv.org/abs/2211.16116.
Addor, Nans, Andrew J. Newman, Naoki Mizukami, and Martyn P. Clark. 2017. “The CAMELS Data Set: Catchment Attributes and Meteorology for Large-Sample Studies.” Hydrology and Earth System Sciences 21 (10): 5293–313. https://doi.org/10.5194/hess-21-5293-2017.
Aich, Michael, Philipp Hess, Baoxiang Pan, Sebastian Bathiany, Yu Huang, and Niklas Boers. 2026. “Conditional Diffusion Models for Downscaling and Bias Correction of Earth System Model Precipitation.” Geoscientific Model Development 19 (4): 1791–808. https://doi.org/10.5194/gmd-19-1791-2026.
Armstrong McKay, David I., Arie Staal, Jesse F. Abrams, Ricarda Winkelmann, Boris Sakschewski, Sina Loriani, Ingo Fetzer, Sarah E. Cornell, Johan Rockstrom, et al. 2022. “Exceeding 1.5°c Global Warming Could Trigger Multiple Climate Tipping Points.” Science 377 (6611): eabn7950. https://doi.org/10.1126/science.abn7950.
Armstrong McKay, David I., Arie Staal, Jesse F. Abrams, Ricarda Winkelmann, Boris Sakschewski, Sina Loriani, Ingo Fetzer, Sarah E. Cornell, Johan Rockström, et al. 2022. “Exceeding 1.5°c Global Warming Could Trigger Multiple Climate Tipping Points.” Science 377 (6611): eabn7950. https://doi.org/10.1126/science.abn7950.
Arsenault, Richard, François Brissette, Jean-Luc Martel, et al. 2020. “A Comprehensive, Multisource Database for Hydrometeorological Modeling of 14,425 North American Watersheds.” Scientific Data 7: 243. https://doi.org/10.1038/s41597-020-00583-2.
Beck, Maximilian, Korbinian Poeschel, Markus Spanring, et al. 2024. “xLSTM: Extended Long Short-Term Memory.” arXiv Preprint arXiv:2405.04517. https://arxiv.org/abs/2405.04517.
Bednar-Friedl, Birgit, Robbert Biesbroek, Daniela N. Schmidt, et al. 2022. “Europe.” In Climate Change 2022: Impacts, Adaptation and Vulnerability. Cambridge University Press. https://doi.org/10.1017/9781009325844.015.
Berkeley Earth. 2024. Global Temperature Data. https://berkeleyearth.org/data/.
BMWK. n.d. The Electricity Market of the Future. Federal Ministry for Economic Affairs; Climate Action; https://www.bmwk.de/Redaktion/EN/Dossier/electricity-market-of-the-future.html.
Boers, Niklas, and Martin Rypdal. 2021. “Critical Slowing down Suggests That the Western Greenland Ice Sheet Is Close to a Tipping Point.” Proceedings of the National Academy of Sciences 118 (21): e2024192118. https://doi.org/10.1073/pnas.2024192118.
Cannon, Alex J. 2018. “Multivariate Quantile Mapping Bias Correction: An N-Dimensional Probability Density Function Transform for Climate Model Simulations of Multiple Variables.” Climate Dynamics 50 (1–2): 31–49. https://doi.org/10.1007/s00382-017-3580-6.
Chagas, Vinicius B. P., Pedro L. B. Chaffe, Nans Addor, et al. 2020. “CAMELS-BR: Hydrometeorological Time Series and Landscape Attributes for 897 Catchments in Brazil.” Earth System Science Data 12: 2075–96. https://doi.org/10.5194/essd-12-2075-2020.
Chen, Yiling, Zhiying Su, R. Iestyn Woolway, et al. 2026. “Persistent River Heatwaves Are Emerging Worldwide Under Climate Change.” Nature Communications 17: 94. https://doi.org/10.1038/s41467-025-66868-5.
Chen, Ying, Huanping Wu, Nengfu Xie, et al. 2025. “STAT-LSTM: A Multivariate Spatiotemporal Feature Aggregation Model for SPEI-based Drought Prediction.” Earth Science Informatics 18 (3): 289. https://doi.org/10.1007/s12145-025-01813-0.
Cleveland, Robert B., William S. Cleveland, Jean E. McRae, and Irma Terpenning. 1990. “STL: A Seasonal-Trend Decomposition Procedure Based on Loess.” Journal of Official Statistics 6 (1): 3–73. https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/stl-a-seasonal-trend-decomposition-procedure-based-on-loess.pdf.
Coxon, Gemma, Nans Addor, John P. Bloomfield, et al. 2020. “CAMELS-GB: Hydrometeorological Time Series and Landscape Attributes for 671 Catchments in Great Britain.” Earth System Science Data 12: 2459–83. https://doi.org/10.5194/essd-12-2459-2020.
Dakos, Vasilis, Stephen R. Carpenter, William A. Brock, et al. 2012. “Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data.” PLoS ONE 7 (7): e41010. https://doi.org/10.1371/journal.pone.0041010.
Denmark.dk. n.d. Denmark Is a Laboratory for Green Solutions. Ministry of Foreign Affairs of Denmark; https://denmark.dk/innovation-and-design/green-solutions/.
Deser, Clara, Flavio Lehner, Keith B. Rodgers, et al. 2020. “Insights from Earth System Model Initial-Condition Large Ensembles and Future Prospects.” Nature Climate Change 10 (4): 277–86. https://doi.org/10.1038/s41558-020-0731-2.
Efron, Bradley, and Robert J. Tibshirani. 1993. An Introduction to the Bootstrap. Chapman & Hall/CRC.
Ember. 2026a. Monthly Electricity Data. https://ember-energy.org/data/monthly-electricity-data/.
Ember. 2026b. Yearly Electricity Data. https://ember-energy.org/data/yearly-electricity-data/.
European Commission. 2020. Poland Final National Energy and Climate Plan Summary. https://energy.ec.europa.eu/system/files/2020-01/pl_final_necp_summary_en_0.pdf.
Frame, Jonathan M., Frederik Kratzert, Daniel Klotz, et al. 2022. “Deep Learning Rainfall–Runoff Predictions of Extreme Events.” Hydrology and Earth System Sciences 26 (13): 3377–92. https://doi.org/10.5194/hess-26-3377-2022.
Funk, Henri, Cornelia Gruber, Göran Kauermann, Helmut Küchenhoff, and Magdalena Mittermeier. 2026. Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability. https://arxiv.org/abs/2608.01864.
Gao, Zhihan, Xingjian Shi, Hao Wang, et al. 2023. Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. arXiv:2207.05833. arXiv. https://doi.org/10.48550/arXiv.2207.05833.
Gauch, Martin, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, and Sepp Hochreiter. 2021. “Rainfall–Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network.” Hydrology and Earth System Sciences 25 (4): 2045–62. https://doi.org/10.5194/hess-25-2045-2021.
Geels, Frank W., and Martina Ayoub. 2023. “A Socio-Technical Transition Perspective on Positive Tipping Points in Climate Change Mitigation: Analysing Seven Interacting Feedback Loops in Offshore Wind and Electric Vehicles Acceleration.” Technological Forecasting and Social Change 193: 122639. https://doi.org/10.1016/j.techfore.2023.122639.
Gneiting, Tilmann, and Adrian E. Raftery. 2007. “Strictly Proper Scoring Rules, Prediction, and Estimation.” Journal of the American Statistical Association 102 (477): 359–78. https://doi.org/10.1198/016214506000001437.
GOV.UK. 2024. Clean Power 2030 Action Plan. https://www.gov.uk/government/publications/clean-power-2030-action-plan.
Gruber, Cornelia, Henri Funk, Magdalena Mittermeier, Helmut Küchenhoff, and Göran Kauermann. 2026. “Capturing Aleatoric Uncertainty in Climate Models.” Environmetrics 37 (5): e70108. https://doi.org/10.1002/env.70108.
Gruber, Cornelia, Patrick Oliver Schenk, Malte Schierholz, Frauke Kreuter, and Göran Kauermann. 2025. Sources of Uncertainty in Supervised Machine Learning – a Statisticians’ View. https://arxiv.org/abs/2305.16703.
Gu, Albert, and Tri Dao. 2023. “Mamba: Linear-Time Sequence Modeling with Selective State Spaces.” arXiv Preprint arXiv:2312.00752. https://arxiv.org/abs/2312.00752.
Hamel, A. van, G. Bruno, C. Chartier-Rescan, et al. 2025. “Riverine Heatwaves Are an Emergent Climate Change Risk.” Nature Water 3: 1356–64. https://doi.org/10.1038/s44221-025-00541-5.
Hawkins, Ed, and Rowan Sutton. 2009. “The Potential to Narrow Uncertainty in Regional Climate Predictions.” Bulletin of the American Meteorological Society 90 (8): 1095–108. https://doi.org/10.1175/2009BAMS2607.1.
Hawkins, Ed, and Rowan Sutton. 2012. “Time of Emergence of Climate Signals.” Geophysical Research Letters 39: L01702. https://doi.org/10.1029/2011GL050087.
Hobday, Alistair J., Lisa V. Alexander, Sarah E. Perkins, et al. 2016. “A Hierarchical Approach to Defining Marine Heatwaves.” Progress in Oceanography 141: 227–38. https://doi.org/10.1016/j.pocean.2015.12.014.
Hochreiter, Sepp, and Juergen Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.
Hoedt, Pieter-Jan, Frederik Kratzert, Daniel Klotz, et al. 2021. “MC-LSTM: Mass-Conserving LSTM.” Proceedings of the 38th International Conference on Machine Learning (ICML), 4275–86. https://arxiv.org/abs/2101.05186.
Höge, Marvin, Martina Kauzlaric, Rosi Siber, et al. 2023. “CAMELS-CH: Hydro-Meteorological Time Series and Landscape Attributes for 331 Catchments in Hydrologic Switzerland.” Earth System Science Data 15: 5755–84. https://doi.org/10.5194/essd-15-5755-2023.
Hooker, Giles, Lucas Mentch, and Siyu Zhou. 2021. Unrestricted Permutation Forces Extrapolation: Variable Importance Requires at Least One More Model, or There Is No Free Variable Importance. arXiv:1905.03151. arXiv. https://doi.org/10.48550/arXiv.1905.03151.
Hurrell, James W. 1995. “Decadal Trends in the North Atlantic Oscillation: Regional Temperatures and Precipitation.” Science 269 (5224): 676–79. https://doi.org/10.1126/science.269.5224.676.
IPCC. 2021a. 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 Valérie Masson-Delmotte, Panmao Zhai, Anna Pirani, et al. Cambridge University Press. https://doi.org/10.1017/9781009157896.
IPCC. 2021b. “Global Carbon and Other Biogeochemical Cycles and Feedbacks.” In Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
Iturbide, Maialen, José M. Gutiérrez, Lincoln M. Alves, et al. 2020. “An Update of IPCC Climate Reference Regions for Subcontinental Analysis of Climate Model Data: Definition and Aggregated Datasets.” Earth System Science Data 12 (4): 2959–70. https://doi.org/10.5194/essd-12-2959-2020.
Jakhmola, A., J. Jewell, V. Vinichenko, and A. Cherp. 2026. “Probabilistic Projections of Global Wind and Solar Power Growth Based on Historical National Experience.” Nature Energy 11 (5): 743–55. https://doi.org/10.1038/s41560-026-02021-w.
Jolliffe, Ian T., and David B. Stephenson, eds. 2003. Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley & Sons.
Karras, Tero, Miika Aittala, Timo Aila, and Samuli Laine. 2022. “Elucidating the Design Space of Diffusion-Based Generative Models.” Advances in Neural Information Processing Systems 35: 26565–77. https://arxiv.org/abs/2206.00364.
Killick, Rebecca, Paul Fearnhead, and Idris A. Eckley. 2012. “Optimal Detection of Changepoints with a Linear Computational Cost.” Journal of the American Statistical Association 107 (500): 1590–98. https://doi.org/10.1080/01621459.2012.737745.
Klingler, Christoph, Karsten Schulz, and Mathew Herrnegger. 2021. “LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe.” Earth System Science Data 13: 4529–65. https://doi.org/10.5194/essd-13-4529-2021.
Kratzert, Frederik, Martin Gauch, Grey Nearing, and Daniel Klotz. 2022. “NeuralHydrology — A Python Library for Deep Learning Research in Hydrology.” Journal of Open Source Software 7 (71): 4050. https://doi.org/10.21105/joss.04050.
Kratzert, Frederik, Daniel Klotz, Claire Brenner, Karsten Schulz, and Mathew Herrnegger. 2018. “Rainfall–Runoff Modelling Using Long Short-Term Memory (LSTM) Based Neural Networks.” Hydrology and Earth System Sciences 22 (11): 6005–22. https://doi.org/10.5194/hess-22-6005-2018.
Kratzert, Frederik, Daniel Klotz, Mathew Herrnegger, Alden K. Sampson, Sepp Hochreiter, and Grey S. Nearing. 2019. “Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine Learning Applied to Large-Sample Datasets.” Hydrology and Earth System Sciences 23 (12): 5089–110. https://doi.org/10.5194/hess-23-5089-2019.
Kratzert, Frederik, Grey Nearing, Nans Addor, et al. 2023. “Caravan – a Global Community Dataset for Large-Sample Hydrology.” Scientific Data 10: 61. https://doi.org/10.1038/s41597-023-01975-w.
Kratzert, Frederik, Grey Nearing, Jonathan Frame, et al. 2023. “Caravan – A Global Community Dataset for Large-Sample Hydrology.” Scientific Data 10 (1): 61. https://doi.org/10.1038/s41597-023-01975-w.
Lehner, Flavio, Clara Deser, Nicola Maher, et al. 2020. “Partitioning Climate Projection Uncertainty with Multiple Large Ensembles and CMIP5/6.” Earth System Dynamics 11 (2): 491–508. https://doi.org/10.5194/esd-11-491-2020.
Lenton, Timothy M. et al. 2023. The Global Tipping Points Report 2023. University of Exeter. https://global-tipping-points.org/.
Lenton, Timothy M., Hermann Held, Elmar Kriegler, et al. 2008. “Tipping Elements in the Earth’s Climate System.” Proceedings of the National Academy of Sciences 105 (6): 1786–93. https://doi.org/10.1073/pnas.0705414105.
Lenton, Timothy M., Thomas W. R. Powell, Steven R. Smith, et al. 2026. “A Method to Identify Positive Tipping Points to Accelerate Low-Carbon Transitions and Actions to Trigger Them.” Sustainability Science 21 (1): 201–20. https://doi.org/10.1007/s11625-025-01704-9.
Ling, Fenghua, Zeyu Lu, Jing-Jia Luo, et al. 2024. “Diffusion Model-Based Probabilistic Downscaling for 180-Year East Asian Climate Reconstruction.” Npj Climate and Atmospheric Science 7 (1): 131. https://doi.org/10.1038/s41612-024-00679-1.
Lionello, P., P. Malanotte-Rizzoli, R. Boscolo, et al. 2006. “The Mediterranean Climate: An Overview of the Main Characteristics and Issues.” In Mediterranean, edited by P. Lionello, P. Malanotte-Rizzoli, and R. Boscolo, vol. 4. Developments in Earth and Environmental Sciences. Elsevier. https://doi.org/10.1016/S1571-9197(06)80003-0.
López-Gómez, Ignacio, Zhong Yi Wan, Leonardo Zepeda-Núñez, Tapio Schneider, John Anderson, and Fei Sha. 2025. “Dynamical-Generative Downscaling of Climate Model Ensembles.” Proceedings of the National Academy of Sciences 122 (17): e2420288122. https://doi.org/10.1073/pnas.2420288122.
Maher, Nicola, Adam S. Phillips, Clara Deser, et al. 2025. “The Updated Multi-Model Large Ensemble Archive and the Climate Variability Diagnostics Package: New Tools for the Study of Climate Variability and Change.” Geoscientific Model Development 18: 6341–65. https://doi.org/10.5194/gmd-18-6341-2025.
Mannerfelt, Erik Schytt, Amaury Dehecq, Romain Hugonnet, et al. 2022. “Halving of Swiss Glacier Volume Since 1931 Observed from Terrestrial Image Photogrammetry.” The Cryosphere 16: 3249–68. https://doi.org/10.5194/tc-16-3249-2022.
Mardani, Morteza, Noah Brenowitz, Yair Cohen, et al. 2025. “Residual Corrective Diffusion Modeling for Km-Scale Atmospheric Downscaling.” Communications Earth & Environment 6 (1): 124. https://doi.org/10.1038/s43247-025-02042-5.
Marusov, Alexander, Vsevolod Grabar, Yury Maximov, Nazar Sotiriadi, Alexander Bulkin, and Alexey Zaytsev. 2024. “Long-Term Drought Prediction Using Deep Neural Networks Based on Geospatial Weather Data.” Environmental Modelling & Software 179 (August): 106127. https://doi.org/10.1016/j.envsoft.2024.106127.
Matiu, Michael, Alice Crespi, Giacomo Bertoldi, et al. 2021. “Observed Snow Depth Trends in the European Alps: 1971 to 2019.” The Cryosphere 15: 1343–82. https://doi.org/10.5194/tc-15-1343-2021.
McKee, Thomas B., Nolan J. Doesken, and John Kleist. 1993. “The Relationship of Drought Frequency and Duration to Time Scales.” Proceedings of the 8th Conference on Applied Climatology 17: 179–83.
McKinnon, Karen A., and Clara Deser. 2018. “Internal Variability and Regional Climate Trends in an Observational Large Ensemble.” Journal of Climate 31: 6783–802. https://doi.org/10.1175/JCLI-D-17-0901.1.
McKinnon, Karen A., and Clara Deser. 2021. “The Inherent Uncertainty of Precipitation Variability, Trends, and Extremes Due to Internal Variability, with Implications for Western u.s. Water Resources.” Journal of Climate 34: 9605–22. https://doi.org/10.1175/JCLI-D-21-0251.1.
Mercure, Jean-François. 2012. “FTT:Power: A Global Model of the Power Sector with Induced Technological Change and Natural Resource Depletion.” Energy Policy 48: 799–811. https://doi.org/10.1016/j.enpol.2012.06.025.
Mercure, Jean-François, Aileen Lam, Joshua E. Buxton, Chris A. Boulton, Amir Akther, and Timothy M. Lenton. 2026. “Evidence of a Cascading Positive Tipping Point Towards Electric Vehicles.” Nature Communications 17: 240. https://doi.org/10.1038/s41467-025-66945-9.
Merizzi, Fabio, Andrea Asperti, and Stefano Colamonaco. 2024. “Wind Speed Super-Resolution and Validation: From ERA5 to CERRA via Diffusion Models.” Neural Computing and Applications 36 (34): 21899–921. https://doi.org/10.1007/s00521-024-10139-9.
Milinski, Sebastian, Nicola Maher, and Dirk Olonscheck. 2020. “How Large Does a Large Ensemble Need to Be?” Earth System Dynamics 11: 885–901. https://doi.org/10.5194/esd-11-885-2020.
Millot, Claude, and Isabelle Taupier-Letage. 2005. “Circulation in the Mediterranean Sea.” In The Mediterranean Sea, edited by Alain Saliot. Springer Berlin Heidelberg. https://doi.org/10.1007/b107143.
Milly, P. C. D., Julio Betancourt, Malin Falkenmark, et al. 2008. “Stationarity Is Dead: Whither Water Management?” Science 319 (5863): 573–74. https://doi.org/10.1126/science.1151915.
MITECO. 2024. Plan Nacional Integrado de Energía y Clima 2023–2030. https://www.miteco.gob.es/es/energia/estrategia-normativa/pniec-23-30.html.
Molnar, Christoph. 2025. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 3rd ed. Christoph Molnar. https://christophm.github.io/interpretable-ml-book.
Nandgude, Neeta, T. P. Singh, Sachin Nandgude, and Mukesh Tiwari. 2023. “Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies.” Sustainability 15 (15): 11684. https://doi.org/10.3390/su151511684.
Nguyen, Tung, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, and Aditya Grover. 2023. ClimaX: A Foundation Model for Weather and Climate. arXiv:2301.10343. arXiv. https://doi.org/10.48550/arXiv.2301.10343.
Noël, Brice, Leo van Kampenhout, Jan T. M. Lenaerts, Willem Jan van de Berg, and Michiel R. van den Broeke. 2021. “A 21st Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss.” Geophysical Research Letters 48 (5): e2020GL090471. https://doi.org/10.1029/2020GL090471.
O’Neill, Brian C., Claudia Tebaldi, Detlef P. van Vuuren, et al. 2016. “The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6.” Geoscientific Model Development 9 (9): 3461–82. https://doi.org/10.5194/gmd-9-3461-2016.
Pathak, Jaideep, Shashank Subramanian, Peter Harrington, et al. 2022. FourCastNet: A Global Data-driven High-resolution Weather Model Using Adaptive Fourier Neural Operators. arXiv:2202.11214. arXiv. https://doi.org/10.48550/arXiv.2202.11214.
Perez, Ethan, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. 2018. “FiLM: Visual Reasoning with a General Conditioning Layer.” Proceedings of the AAAI Conference on Artificial Intelligence 32 (April). https://doi.org/10.1609/aaai.v32i1.11671.
Rietkerk, Max, Vanessa Skiba, Els Weinans, Raphaël Hébert, and Thomas Laepple. 2025. “Ambiguity of Early Warning Signals for Climate Tipping Points.” Nature Climate Change 15 (5): 479–88. https://doi.org/10.1038/s41558-025-02328-8.
Robinson, Alexander, Reinhard Calov, and Andrey Ganopolski. 2012. “Multistability and Critical Thresholds of the Greenland Ice Sheet.” Nature Climate Change 2 (6): 429–32. https://doi.org/10.1038/nclimate1449.
Rodgers, Keith B., Sun-Seon Lee, Nan Rosenbloom, et al. 2021. “Ubiquity of Human-Induced Changes in Climate Variability.” Earth System Dynamics 12: 1393–411. https://doi.org/10.5194/esd-12-1393-2021.
Rogers, Everett M. 2003. Diffusion of Innovations. 5th ed. Free Press.
Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (Cham), Lecture notes in computer science, vol. 9351: 234–41. https://doi.org/10.1007/978-3-319-24574-4_28.
Rumpf, Sabine B., Mathieu Gravey, Olivier Brönnimann, et al. 2022. “From White to Green: Snow Cover Loss and Increased Vegetation Productivity in the European Alps.” Science 376: 1119–22. https://doi.org/10.1126/science.abn6697.
Sadayappan, Kayalvizhi, and Li Li. 2025. “Riverine Heat Waves on the Rise, Outpacing Air Heat Waves.” Proceedings of the National Academy of Sciences 122 (39): e2503160122. https://doi.org/10.1073/pnas.2503160122.
Saravanan, V., Gordon J. Berman, and Samuel J. Sober. 2020. “Application of the Hierarchical Bootstrap to Multi-Level Data in Neuroscience.” Neurons, Behavior, Data Analysis, and Theory 3 (5): 1–25.
Scheffer, Marten, Jordi Bascompte, William A. Brock, et al. 2009. “Early-Warning Signals for Critical Transitions.” Nature 461 (7260): 53–59. https://doi.org/10.1038/nature08227.
Schmocker-Fackel, Petra, and Felix Naef. 2010. “More Frequent Flooding? Changes in Flood Frequency in Switzerland Since 1850.” Journal of Hydrology 381: 1–8. https://doi.org/10.1016/j.jhydrol.2009.09.022.
Sen, Pranab Kumar. 1968. “Estimates of the Regression Coefficient Based on Kendall’s Tau.” Journal of the American Statistical Association 63: 1379–89. https://doi.org/10.1080/01621459.1968.10480934.
Sharpe, Simon, and Timothy M. Lenton. 2021. “Upward-Scaling Tipping Cascades to Meet Climate Goals: Plausible Grounds for Hope.” Climate Policy 21 (4): 421–33. https://doi.org/10.1080/14693062.2020.1870097.
Song, Yang, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021. “Score-Based Generative Modeling Through Stochastic Differential Equations.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2011.13456.
Springenberg, Maximilian, Noelia Otero, Yuxin Xue, and Jackie Ma. 2026. “DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts Using Diffusion Models.” Journal of Advances in Modeling Earth Systems 18 (3): e2025MS005282. https://doi.org/10.1029/2025MS005282.
Swart, Neil C., Jason N. S. Cole, Viatcheslav V. Kharin, et al. 2019. “The Canadian Earth System Model Version 5 (CanESM5.0.3).” Geoscientific Model Development 12 (11): 4823–73. https://doi.org/10.5194/gmd-12-4823-2019.
Tebaldi, Claudia, and Reto Knutti. 2007. “The Use of the Multi-Model Ensemble in Probabilistic Climate Projections.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365 (1857): 2053–75. https://doi.org/10.1098/rsta.2007.2076.
Tomasi, Elena, Gabriele Franch, and Marco Cristoforetti. 2025. “Can AI Be Enabled to Perform Dynamical Downscaling? A Latent Diffusion Model to Mimic Kilometer-Scale COSMO5.0_CLM9 Simulations.” Geoscientific Model Development 18 (6): 2051–78. https://doi.org/10.5194/gmd-18-2051-2025.
Truong, Charles, Laurent Oudre, and Nicolas Vayatis. 2020. “Selective Review of Offline Change Point Detection Methods.” Signal Processing 167: 107299. https://doi.org/10.1016/j.sigpro.2019.107299.
Van den Bulte, Christophe, and Gary L. Lilien. 1997. “Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models.” Marketing Science 16 (4): 338–53. https://doi.org/10.1287/mksc.16.4.338.
Vicente-Serrano, Sergio M., Santiago Beguería, and Juan I. López-Moreno. 2010. “A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index.” Journal of Climate 23 (7): 1696–718. https://doi.org/10.1175/2009JCLI2909.1.
Wang, Tian, Xinjun Tu, Vijay P. Singh, Xiaohong Chen, Kairong Lin, and Zonglin Zhou. 2023. “Drought Prediction: Insights from the Fusion of LSTM and Multi-Source Factors.” Science of The Total Environment 902 (December): 166361. https://doi.org/10.1016/j.scitotenv.2023.166361.
Watt, Robbie A., and Laura A. Mansfield. 2024. Generative Diffusion-Based Downscaling for Climate. https://arxiv.org/abs/2404.17752.
Wilks, Daniel S. 2011. Statistical Methods in the Atmospheric Sciences. Elsevier Academic Press. https://www.sciencedirect.com/bookseries/international-geophysics/vol/100/suppl/C.
Xu, Ruiyu, Zheren Song, Jianguo Wu, Chao Wang, and Shiyu Zhou. 2025. “Change-Point Detection with Deep Learning: A Review.” Frontiers of Engineering Management 12 (1): 154–76. https://doi.org/10.1007/s42524-025-4109-z.
Yu, Jiaxin, Tinghuai Ma, Li Jia, Huan Rong, Yuming Su, and Mohamed Magdy Abdel Wahab. 2023. “Multivariate Spatio-Temporal Modeling of Drought Prediction Using Graph Neural Network.” Journal of Hydroinformatics 26 (1): 107–24. https://doi.org/10.2166/hydro.2023.134.
Zargar, Amin, Rehan Sadiq, Bahman Naser, and Faisal I. Khan. 2011. “A Review of Drought Indices.” Environmental Reviews 19: 333–49. https://www.jstor.org/stable/envirevi.19.333.
Zeiler, Matthew D., and Rob Fergus. 2013. Visualizing and Understanding Convolutional Networks. arXiv:1311.2901. arXiv. https://doi.org/10.48550/arXiv.1311.2901.
Zeng, Ailing, Muxi Chen, Lei Zhang, and Qiang Xu. 2022. Are Transformers Effective for Time Series Forecasting? arXiv:2205.13504. arXiv. https://doi.org/10.48550/arXiv.2205.13504.
Zhang, Aston, Zachary C. Lipton, Mu Li, and Alexander J. Smola. 2023. Dive into Deep Learning. Cambridge University Press.