66 citations as recorded by crossref.

1. Human activity and simultaneous high-pressure anomalies influence the long-duration cold events of winter in China L. Zhao et al. https://doi.org/10.1007/s00382-023-06719-5
2. Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails M. Krock et al. https://doi.org/10.1016/j.wace.2022.100438
3. Fast parameter estimation of generalized extreme value distribution using neural networks S. Rai et al. https://doi.org/10.1002/env.2845
4. A virtual climate library of surface temperature over North America for 1979–2015 S. Kravtsov et al. https://doi.org/10.1038/sdata.2017.155
5. Nonstationary generalized extreme value modeling of extreme wind speed events with seasonality and time-varying variability J. Aboy et al. https://doi.org/10.1007/s11069-026-08193-8
6. Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles E. Hogan et al. https://doi.org/10.1175/JCLI-D-18-0075.1
7. Assessment of temporal changes in frequency characteristics of annual maximum rainfall of daily duration over Bangladesh S. Das & A. Islam https://doi.org/10.1007/s00704-023-04479-0
8. A generalized extreme value approach for the analysis of stationary climatic covariate in a Mediterranean city S. Cherif https://doi.org/10.1002/asl.1291
9. Comparison of Future Changes in Frequency of Climate Extremes between Coastal and Inland Locations of Bengal Delta Based on CMIP6 Climate Models S. Das et al. https://doi.org/10.3390/atmos13111747
10. Boosting ensembles for statistics of tails at conditionally optimal advance split times J. Finkel & P. O'Gorman https://doi.org/10.5194/npg-33-233-2026
11. Practical strategies for generalized extreme value‐based regression models for extremes D. Castro‐Camilo et al. https://doi.org/10.1002/env.2742
12. Understanding the detectability of potential changes to the 100-year peak storm surge R. Ceres et al. https://doi.org/10.1007/s10584-017-2075-0
13. On estimating long period wind speed return levels from annual maxima M. Ben Alaya et al. https://doi.org/10.1016/j.wace.2021.100388
14. Neural networks for parameter estimation in intractable models A. Lenzi et al. https://doi.org/10.1016/j.csda.2023.107762
15. Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” A. Poppick https://doi.org/10.1007/s13253-023-00537-2
16. Quantifying the influence of global warming on unprecedented extreme climate events N. Diffenbaugh et al. https://doi.org/10.1073/pnas.1618082114
17. Daily Precipitation and Temperature Extremes in Southern Italy (Calabria Region) G. Prete et al. https://doi.org/10.3390/atmos14030553
18. Convection‐permitting climate simulations with COSMO‐CLM over northwestern Türkiye under the RCP8.5 scenario C. Sonuç et al. https://doi.org/10.1002/joc.8061
19. Record-breaking marine heatwave in northern Yellow Sea during summer 2018: Characteristics, drivers and ecological impact Y. Li et al. https://doi.org/10.1016/j.scitotenv.2023.166385
20. How Unexpected Was the 2021 Pacific Northwest Heatwave? K. McKinnon & I. Simpson https://doi.org/10.1029/2022GL100380
21. Distributed Inference for Spatial Extremes Modeling in High Dimensions E. Hector & B. Reich https://doi.org/10.1080/01621459.2023.2186886
22. Extreme value modeling with errors-in-variables in detection and attribution of changes in climate extremes Y. Lau et al. https://doi.org/10.1007/s11222-023-10290-8
23. Spatio‐Temporal Analysis of Extreme PM2.5 Levels in Taiwan B. Jhou et al. https://doi.org/10.1002/env.70068
24. Detecting Extreme Temperature Events Using Gaussian Mixture Models A. Paçal et al. https://doi.org/10.1029/2023JD038906
25. Spatiotemporal Analysis of Extreme Winter Temperatures in Ireland D. Healy et al. https://doi.org/10.1002/env.70112
26. An Evaluation of Block-Maximum-Based Estimation of Very Long Return Period Precipitation Extremes with a Large Ensemble Climate Simulation M. Ben Alaya et al. https://doi.org/10.1175/JCLI-D-19-0011.1
27. How short climate records affect GEV-based estimates of rare event probabilities R. Patel & T. Schneider https://doi.org/10.1088/2752-5295/ae71fb
28. Historical and future maximum sea surface temperatures B. Cael et al. https://doi.org/10.1126/sciadv.adj5569
29. Extreme Precipitation and Flood Estimation Using Large Ensemble Climate Model Outputs: A Review T. Tanaka et al. https://doi.org/10.1061/JHYEFF.HEENG-6665
30. Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability L. Bird et al. https://doi.org/10.1038/s43247-023-01142-4
31. Forecast-based attribution of the role of stratospheric variability in weather extremes W. Seviour et al. https://doi.org/10.5194/wcd-7-1405-2026
32. Assessment and future projections of storm surge using CMIP6 models in the Indo-Pacific region K. Saha et al. https://doi.org/10.1016/j.ocemod.2025.102560
33. A multivariate analysis of atmospheric drivers for Western European heatwaves A. Paçal et al. https://doi.org/10.5194/esd-17-955-2026
34. Extreme heat and mortality in the state of Rio de Janeiro in November 2023: attribution to climate change and ENSO S. Collazo et al. https://doi.org/10.5194/nhess-25-3221-2025
35. Estimating concurrent climate extremes: A conditional approach W. Huang et al. https://doi.org/10.1016/j.wace.2021.100332
36. Recent heatwaves as a prelude to climate extremes in the western Mediterranean region E. Tejedor et al. https://doi.org/10.1038/s41612-024-00771-6
37. Bayesian hierarchical modelling of intensity-duration-frequency curves using a climate model large ensemble A. Rischmuller et al. https://doi.org/10.5194/ascmo-12-1-2026
38. Multivariate probabilistic estimates of heat stress for rice across China L. Zhang et al. https://doi.org/10.1007/s00477-018-1572-7
39. Functional time series forecasting of extreme values H. Shang & R. Xu https://doi.org/10.1080/23737484.2020.1869629
40. Some Statistical Issues in Climate Science M. Stein https://doi.org/10.1214/19-STS730
41. Extreme value analysis of precipitation and temperature over western Indian Himalayan State, Uttarakhand C. Pandey et al. https://doi.org/10.1007/s12040-023-02057-6
42. Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression M. Haugen et al. https://doi.org/10.1175/JCLI-D-17-0782.1
43. Should annual maximum temperatures follow a generalized extreme value distribution? M. Stein https://doi.org/10.1093/biomet/asw070
44. Unprecedented climate events: Historical changes, aspirational targets, and national commitments N. Diffenbaugh et al. https://doi.org/10.1126/sciadv.aao3354
45. Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change J. Zeder & E. Fischer https://doi.org/10.5194/ascmo-9-83-2023
46. Showcasing MESMER‐X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models Y. Quilcaille et al. https://doi.org/10.1029/2022GL099012
47. Exponential increases in high-temperature extremes in North America A. Davariashtiyani et al. https://doi.org/10.1038/s41598-023-41347-3
48. Estimating precipitation extremes using the log‐histospline W. Huang et al. https://doi.org/10.1002/env.2543
49. Changes in marine hot and cold extremes in the China Seas during 1982–2020 Y. Li et al. https://doi.org/10.1016/j.wace.2023.100553
50. Effect of urbanisation on extreme precipitation based on nonstationary models in the Yangtze River Delta metropolitan region M. Lu et al. https://doi.org/10.1016/j.scitotenv.2019.03.413
51. Risks of temperature extremes over China under 1.5 °C and 2 °C global warming C. Shi et al. https://doi.org/10.1016/j.accre.2020.09.006
52. Composite likelihood methods for histogram-valued random variables T. Whitaker et al. https://doi.org/10.1007/s11222-020-09955-5
53. Very rare heat extremes: quantifying and understanding using ensemble re-initialization C. Gessner et al. https://doi.org/10.1175/JCLI-D-20-0916.1
54. A multi-method framework for global real-time climate attribution D. Gilford et al. https://doi.org/10.5194/ascmo-8-135-2022
55. High‐Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States Z. Zobel et al. https://doi.org/10.1002/2017EF000642
56. New Exploratory Tools for Extremal Dependence: $$\chi $$ Networks and Annual Extremal Networks W. Huang et al. https://doi.org/10.1007/s13253-019-00356-4
57. Julia for HPC: In Situ Data Analysis with Julia for Climate Simulations at Large Scale L. Tang et al. https://doi.org/10.21105/jcon.00134
58. A multivariate copula framework quantifies the augmenting joint risks of compound high temperature and dry/wet events under climate change in China M. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.134321
59. Quantifying Uncertainty Due to Stochastic Weather Generators in Climate Change Impact Studies F. Vesely et al. https://doi.org/10.1038/s41598-019-45745-4
60. Modeling short‐ranged dependence in block extrema with application to polar temperature data B. Russell & W. Huang https://doi.org/10.1002/env.2661
61. Seasonal and Station Effects Modelling to Extreme Temperature Data in South Africa T. Diriba & L. Debusho https://doi.org/10.1007/s10666-023-09881-6
62. Non-stationary temperature extremes in South Korea: An extreme value analysis of global warming impacts J. Ryu et al. https://doi.org/10.1016/j.atmosres.2025.108388
63. GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy M. Gentilucci et al. https://doi.org/10.3390/su151511656
64. Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation V. Ahuja et al. https://doi.org/10.1007/s12648-023-03011-4
65. On vulnerabilities in EVT-based timing analysis: an experimental investigation on a multi-core architecture J. Vasconcelos et al. https://doi.org/10.1007/s10617-023-09277-5
66. Assessment of climate change impact on temperature extremes in a tropical region with the climate projections from CMIP6 model S. Das et al. https://doi.org/10.1007/s00382-022-06416-9