95 citations as recorded by crossref.

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26. Climate Change Effects on Rainfall Intensity–Duration–Frequency (IDF) Curves for the Lake Erie Coast Using Various Climate Models S. Mainali & S. Sharma https://doi.org/10.3390/w15234063
27. Bias-corrected UKCP18 Convection-permitting model projections for England Q. Zha et al. https://doi.org/10.5194/hess-30-2395-2026
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33. Evaluating changes in flood frequency due to climate change in the Western Cape, South Africa K. Pillay & M. Simatele https://doi.org/10.1007/s00477-024-02786-0
34. Gazing into the flames: A guide to assessing the impacts of climate change on landscape fire H. Clarke et al. https://doi.org/10.1126/sciadv.adz2429
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38. A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China Z. Qi et al. https://doi.org/10.1007/s11356-024-35013-y
39. Projected changes in meteorological drought over East Africa inferred from bias-adjusted CMIP6 models B. Ayugi et al. https://doi.org/10.1007/s11069-022-05341-8
40. Machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting X. Meng et al. https://doi.org/10.1007/s10489-024-05504-z
41. Comparison of precipitation projections of CMIP5 and CMIP6 global climate models over Yulin, China M. Shiru et al. https://doi.org/10.1007/s00704-021-03823-6
42. Assessing climate change impacts on flood risk in the Yeongsan River Basin, South Korea A. Ashu & J. Kang https://doi.org/10.1038/s41598-025-11921-y
43. Wet-environment Evapotranspiration and Precipitation Standardized Index (WEPSI) for drought assessment and monitoring A. Khoshnazar et al. https://doi.org/10.2166/nh.2022.062
44. Multi-Model Ensemble Enhances the Spatiotemporal Comprehensive Performance of Regional Climate in China Y. Wang et al. https://doi.org/10.3390/rs17040582
45. Assessment of climate change impacts on crop and irrigation water demand in the Awash River basin of Ethiopia using CMIP6 models E. Meskelu et al. https://doi.org/10.1007/s43832-025-00307-w
46. Evaluation and prediction of future droughts with multi-model ensembling of four models under CMIP6 scenarios over Iraq Y. Abduljaleel et al. https://doi.org/10.1007/s00704-023-04595-x
47. Catchment‐scale skill assessment of seasonal precipitation forecasts across South Korea Y. Lee et al. https://doi.org/10.1002/joc.8134
48. Comparative analysis of bias correction methods for projecting extreme precipitation and temeprature indices in Pakistan Z. Ali et al. https://doi.org/10.1016/j.atmosres.2025.107957
49. Accuracy Assessment of Atmospheric Large Eddy Simulations to Support Uncrewed Aircraft Systems Operations at GrandSKY, North Dakota C. Wooton et al. https://doi.org/10.3390/atmos17050468
50. Projection of precipitation extremes in China's mainland based on the statistical downscaled data from 27 GCMs in CMIP6 L. Wang et al. https://doi.org/10.1016/j.atmosres.2022.106462
51. A systematic review of climate downscaling and extremes in Coupled Model Intercomparison Project 6 (CMIP6) E. Devadarshini et al. https://doi.org/10.1016/j.envdev.2025.101280
52. Advances in Resilience Assessment and Adaptive Strategies for Watershed Non-Point Source Pollution Systems Under Climate Change B. Liu et al. https://doi.org/10.3390/su18136917
53. Risk assessment of agricultural green water security in Northeast China under climate change J. Sun et al. https://doi.org/10.1007/s11430-023-1278-2
54. Evaluation and comparison of the performances of the CMIP5 and CMIP6 models in reproducing extreme rainfall in the Upper Blue Nile basin of Ethiopia H. Belay et al. https://doi.org/10.1007/s00704-024-05187-z
55. Moving statistic method (MSM) for quantification of changes in precipitation intensity and dry periods caused by climate change and demonstration on major U.S. cities B. Barkdoll https://doi.org/10.1007/s40899-023-00880-9
56. Assessing blue-green infrastructures for urban flood and drought mitigation under changing climate scenarios X. Wu & P. Willems https://doi.org/10.1016/j.ejrh.2025.102798
57. Climate Change and Florida Groundwater Management—Actionable Data and Adaptations R. Maliva https://doi.org/10.3390/w17111572
58. Improving Daily CMIP6 Precipitation in Southern Africa Through Bias Correction—Part 1: Spatiotemporal Characteristics A. Addisuu et al. https://doi.org/10.3390/cli13050095
59. Observed and Future Precipitation and Evapotranspiration in Water Management Zones of Uganda: CMIP6 Projections C. Onyutha et al. https://doi.org/10.3390/atmos12070887
60. Changes in Precipitation Patterns in Poland Derived From Projected Downscaled Future Climate Data From CMIP5 and CMIP6 A. Rutkowska et al. https://doi.org/10.1002/joc.8822
61. Correcting dry/wet classification bias in precipitation downscaling via generative adversarial networks S. Singh et al. https://doi.org/10.1017/eds.2026.10039
62. CMIP6 multi-model projections and glacier-explicit hydrological modelling for seasonal runoff shifts in the Hunza River Basin H. Batool et al. https://doi.org/10.1007/s00703-026-01149-4
63. Lack of clear standards and usable comparisons of downscaled climate projections pose a roadblock for US climate discovery and adaptation S. Hartke et al. https://doi.org/10.1088/1748-9326/adc74e
64. Real-time integrated water availability – Salt intrusion modelling and management during droughts D. Bertels et al. https://doi.org/10.1016/j.jhydrol.2024.131894
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66. Using a two-step downscaling method to assess the impact of climate change on total nitrogen load in a small basin X. Li et al. https://doi.org/10.1016/j.jhydrol.2023.130510
67. Quantifying the impact of climate change and extreme heat on rice in the United States S. Li et al. https://doi.org/10.1016/j.agrformet.2024.110145
68. An assessment of the Statistical Bias Correction Techniques for CSIRO-Mk 3–6-0 model Rainfall and Temperature in Punjab, India A. Krishna et al. https://doi.org/10.1007/s00704-025-05613-w
69. Evaluating the enhanced benefits of multi-model ensemble mean from NEX-GDDP-CMIP6 versus native CMIP6 Models in accurately representing historical temperature patterns in South America H. Arregocés et al. https://doi.org/10.1016/j.envc.2025.101373
70. Methods for assessing climate uncertainty in energy system models — A systematic literature review L. Plaga & V. Bertsch https://doi.org/10.1016/j.apenergy.2022.120384
71. Statistical downscaling of climate variables for local forecasts and applications to improve climate change prediction in upper Blue Nile Basin A. Bulti & G. Yutura https://doi.org/10.1016/j.pce.2025.103867
72. Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River N. Dong et al. https://doi.org/10.5194/hess-29-2023-2025
73. Downscaling algorithms for CMIP6 GCM daily rainfall over India R. Raj et al. https://doi.org/10.1007/s12040-024-02323-1
74. Uncovering the strengths and weaknesses of an ensemble of quantile mapping methods for downscaling precipitation change in Southern Africa S. Mendoza Paz & P. Willems https://doi.org/10.1016/j.ejrh.2022.101104
75. Resilient agriculture: water management for climate change adaptation in Lower Saxony R. Cotera et al. https://doi.org/10.2166/wcc.2024.455
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77. A Comparison of the Statistical Downscaling and Long-Short-Term-Memory Artificial Neural Network Models for Long-Term Temperature and Precipitations Forecasting N. Fouotsa Manfouo et al. https://doi.org/10.3390/atmos14040708
78. Potential impact of precipitation temporal structure on meteorological drought and vegetation condition: A case study on Qinghai-Tibet Plateau H. Lu et al. https://doi.org/10.1016/j.ejrh.2024.102048
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