2026

• Rischmuller, A. L.; Poschlod, B.; Sillmann, J. (2026): Bayesian hierarchical modelling of intensity-duration-frequency curves using a climate model large ensemble, Advances in Statistical Climatology, Meteorology and Oceanography, 12, 1–19. https://doi.org/10.5194/ascmo-12-1-2026
• Poschlod, B.; Sailer, L.; Sasse, A.; Vogelbacher, A.; Ludwig, R. (2026): Climate change effects on river droughts in Bavaria using a hydrological large ensemble, Hydrology and Earth System Sciences, 30, 1165–1188. https://doi.org/10.5194/hess-30-1165-2026
• Gavasso-Rita, Y. L.; Papalexiou, S. M.; Li, Y.; Elshorbagy, A.; Schuster-Wallace, C. (2026): Initial Exploration of Canola Producers' Approaches in Response to Changing Climate in the Canadian Prairie Provinces, Plant-Environment Interactions, 7, e70107. https://doi.org/10.1002/pei3.70107
• Xu, Y.; Frappart, F.; Tang, G.; Zhang, G.; Lin, P.; Jiang, L.; Papalexiou, S. M.; Yao, F.; Han, X.; Xia, J. (2026): A global intercomparison of SWOT and traditional nadir radar altimetry for monitoring river water surface elevation, Remote Sensing of Environment, 334, 115219. https://doi.org/10.1016/j.rse.2025.115219
• Gavasso-Rita, Y. L.; Abdelmoaty, H.; Li, Y.; Papalexiou, S. M. (2026): Performance of current canola (Brassica napus) hybrids under future rainfed production management, Journal of Agriculture and Food Research, 25, 102574. https://doi.org/10.1016/j.jafr.2025.102574
• Marra, F.; Dallan, E.; Canale, A.; Prosdocimi, I.; Papacharalampous, G.; Borga, M.; Papalexiou, S. M. (2026): Apparent Heavy Tails of Sub-Daily Precipitation Explained by the Coexistence of Lighter-Tailed Processes, Geophysical Research Letters, 53, e2025GL119705. https://doi.org/10.1029/2025GL119705
• Papalexiou, S. M.; Mamalakis, A. (2026): Machine unlearning: bias correction in neural network downscaled storms, Journal of Hydrology, 665, 134689. https://doi.org/10.1016/j.jhydrol.2025.134689

2025

• Böhnisch, A.; Felsche, E.; Mittermeier, M.; Poschlod, B.; Ludwig, R. (2025): Future Patterns of Compound Dry and Hot Summers and Their Link to Soil Moisture Droughts in Europe, Earth's Future, 13, e2024EF004916. https://doi.org/10.1029/2024EF004916
• Zabel, F.; Knüttel, M.; Poschlod, B. (2025): CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment, Geoscientific Model Development, 18, 1067–1087. https://doi.org/10.5194/gmd-18-1067-2025
• Brunner, L.; Poschlod, B.; Dutra, E.; Fischer, E. M.; Martius, O.; Sillmann, J. (2025): A global perspective on the spatial representation of climate extremes from km-scale models, Environmental Research Letters, 20, 074054. https://doi.org/10.1088/1748-9326/ade1ef
• Hanf, F. S.; Ament, F.; Boettcher, M.; Burgemeister, F.; Gaslikova, L.; Hoffmann, P.; Knieling, J.; Matthias, V.; Meier, L.; Pein, J.; Poschlod, B.; Quante, M.; Ratzke, L.; Rudolph, E.; Scheffran, J.; Schlünzen, K. H.; Shokri, N.; Sillmann, J.; Vogelbacher, A.; Von Szombathely, M.; Wickel, M. (2025): Towards a socio-ecological system understanding of urban flood risk and barriers to climate change adaptation using causal loop diagrams, International Journal of Urban Sustainable Development, 17, 69–102. https://doi.org/10.1080/19463138.2025.2474399
• Nerantzaki, S. D.; Abdelmoaty, H. M.; Papalexiou, S. M.; Newman, A. J. (2025): The influence of atmospheric drivers, environmental factors, and urban land use on extreme hourly precipitation trends over the CONtiguous United States for 40 years at 4-km resolution (CONUS404), Science of The Total Environment, 969, 178407. https://doi.org/10.1016/j.scitotenv.2025.178407
• Ballarin, A. S.; Markonis, Y.; Rakovec, O.; Papalexiou, S. M. (2025): How likely is Ukraine to experience an extreme drought in the near future?, Climatic Change, 178, 113. https://doi.org/10.1007/s10584-025-03958-9
• Abouzied, G. A. A.; Tang, G.; Papalexiou, S. M.; Clark, M. P.; Aruffo, E.; Di Carlo, P. (2025): Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019, Geoscience Data Journal, 12, e267. https://doi.org/10.1002/gdj3.267
• Tang, G.; Clark, M. P.; Knoben, W. J. M.; Liu, H.; Gharari, S.; Arnal, L.; Wood, A. W.; Newman, A. J.; Freer, J.; Papalexiou, S. M. (2025): Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings, Bulletin of the American Meteorological Society, 106, E146–E166. https://doi.org/10.1175/BAMS-D-24-0007.1
• Papalexiou, S. M.; Mascaro, G.; Pendergrass, A. G.; Mamalakis, A.; de Brito, M. M.; Andreadis, K. M.; Schiro, K.; Zaerpour, M.; Hatami, S.; Gavasso-Rita, Y. L.; Ballarin, A. S.; Godoy, M. R. V.; Nerantzaki, S.; Abdelmoaty, H.; Matin, M. A.; Madani, K. (2025): Sustainability Nexus AID: storms, Sustainability Nexus Forum, 33, 1. https://doi.org/10.1007/s00550-024-00544-y
• Abdelmoaty, H. M.; Rajulapati, C. R.; Nerantzaki, S. D.; Papalexiou, S. M. (2025): Bias-corrected high-resolution temperature and precipitation projections for Canada, Scientific Data, 12, 191. https://doi.org/10.1038/s41597-025-04396-z
• Zaerpour, M.; Hatami, S.; Ballarin, A. S.; Papalexiou, S. M.; Pietroniro, A.; Nazemi, A. (2025): Agriculture’s impact on water–energy balance varies across climates, Proceedings of the National Academy of Sciences, 122, e2410521122. https://doi.org/10.1073/pnas.2410521122
• Vogel, R. M.; , S. M. P.; , J. R. L.; and Dolan, F. C. (2025): When Heavy Tails Disrupt Statistical Inference, The American Statistician, 79, 221–235. https://doi.org/10.1080/00031305.2024.2402898
• Godoy, M. R. V.; Markonis, Y.; Thomson, J. R.; Ballarin, A. S.; Perri, S.; Miao, C.; Sun, Q.; Hanel, M.; Papalexiou, S. M.; Kummerow, C.; Oki, T.; Molini, A. (2025): Which Precipitation Dataset to Choose for Hydrological Studies of the Terrestrial Water Cycle?, Bulletin of the American Meteorological Society, 106, E2000–E2016. https://doi.org/10.1175/BAMS-D-24-0306.1
• Pradhan, R. K.; Markonis, Y.; Marra, F.; Nikolopoulos, E. I.; Papalexiou, S. M.; Levizzani, V. (2025): Diurnal variability of global precipitation: insights from hourly satellite and reanalysis datasets, Hydrology and Earth System Sciences, 29, 4929–4949. https://doi.org/10.5194/hess-29-4929-2025
• Abdelmoaty, H. M.; Papalexiou, S. M.; Mamalakis, A.; Singh, S.; Coia, V.; Hairabedian, M.; Szeftel, P.; Grover, P. (2025): Generative Adversarial Networks for Downscaling Hourly Precipitation in the Canadian Prairies, Journal of Geophysical Research: Machine Learning and Computation, 2, e2025JH000678. https://doi.org/10.1029/2025JH000678
• Gavasso-Rita, Y. L.; Zaerpour, M.; Abdelmoaty, H.; Li, Y.; Elshorbagy, A.; Schuster-Wallace, C.; Paschalis, A.; Papalexiou, S. M. (2025): Rainfed spring canola yield response to changing heat and water stress in the Canadian Prairie region, Agricultural Water Management, 322, 109966. https://doi.org/10.1016/j.agwat.2025.109966
• Zaerpour, M.; Papalexiou, S. M.; Pietroniro, A. (2025): Increasing tree canopy lowers urban air temperature by up to 1.5 °C in heat-prone areas, npj Urban Sustainability, 5, 92. https://doi.org/10.1038/s42949-025-00277-x
• Lombardo, L.; Papalexiou, S. M.; Thébault, C.; Clark, M. P.; Vogel, R. M.; Viglione, A. (2025): Residual dynamics in hydrological models: insights from a large sample of catchments and models, Advances in Water Resources, 206, 105165. https://doi.org/10.1016/j.advwatres.2025.105165
• Hatami, S.; Zaerpour, M.; Ballarin, A. S.; Adamowski, J. F.; Papalexiou, S. M.; Pietroniro, A.; Kimball, J. S. (2025): Declining number of northern hemisphere land-surface frozen days under global warming and thinner snowpacks, Communications Earth & Environment, 7, 29. https://doi.org/10.1038/s43247-025-03059-6

2024

• Lang, A.; Poschlod, B. (2024): Updating catastrophe models to today’s climate – An application of a large ensemble approach to extreme rainfall, Climate Risk Management, 44, 100594. https://doi.org/10.1016/j.crm.2024.100594
• Maroschek, M.; Seidl, R.; Poschlod, B.; Senf, C. (2024): Quantifying patch size distributions of forest disturbances in protected areas across the European Alps, Journal of Biogeography, 51, 368–381. https://doi.org/10.1111/jbi.14760
• Poschlod, B.; Koh, J. (2024): Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels, Water Resources Research, 60, e2023WR035159. https://doi.org/10.1029/2023WR035159
• Orlov, A.; Jägermeyr, J.; Müller, C.; Daloz, A. S.; Zabel, F.; Minoli, S.; Liu, W.; Lin, T.; Jain, A. K.; Folberth, C.; Okada, M.; Poschlod, B.; Smerald, A.; Schneider, J. M.; Sillmann, J. (2024): Human heat stress could offset potential economic benefits of CO2 fertilization in crop production under a high-emissions scenario, One Earth, 7, 1250–1265. https://doi.org/10.1016/j.oneear.2024.06.012
• Poschlod, B.; Daloz, A. S. (2024): Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?, The Cryosphere, 18, 1959–1981. https://doi.org/10.5194/tc-18-1959-2024
• Felsche, E.; Böhnisch, A.; Poschlod, B.; Ludwig, R. (2024): European hot and dry summers are projected to become more frequent and expand northwards, Communications Earth & Environment, 5, 410. https://doi.org/10.1038/s43247-024-01575-5
• Sillmann, J.; Raupach, T. H.; Findell, K. L.; Donat, M.; Alves, L. M.; Alexander, L.; Borchert, L.; De Amorim, P. B.; Buontempo, C.; Fischer, E. M.; Franzke, C. L.; Guan, B.; Haasnoot, M.; Hawkins, E.; Jacob, D.; Mahon, R.; Maraun, D.; Morrison, M. A.; Poschlod, B.; Ruane, A. C.; Shampa, ; Stephenson, T.; Van Der Wel, N.; Wang, Z.; Zhang, X.; Županić, J. (2024): Climate extremes and risks: links between climate science and decision-making, Frontiers in Climate, 6, 1499765. https://doi.org/10.3389/fclim.2024.1499765
• Zaghloul, M. A.; Elshorbagy, A.; Papalexiou, S. M. (2024): Enhancing regional flood frequency analysis by integrating site-similarity measures with watershed modeling, Journal of Hydrology, 641, 131754. https://doi.org/10.1016/j.jhydrol.2024.131754
• Grimaldi, S.; Cappelli, F.; Papalexiou, S. M.; Petroselli, A.; Nardi, F.; Annis, A.; Piscopia, R.; Tauro, F.; Apollonio, C. (2024): Optimizing sensor location for the parsimonious design of flood early warning systems, Journal of Hydrology X, 24, 100182. https://doi.org/10.1016/j.hydroa.2024.100182
• Volpi, E.; Grimaldi, S.; Aghakouchak, A.; Castellarin, A.; Chebana, F.; Papalexiou, S. M.; Aksoy, H.; Bárdossy, A.; Cancelliere, A.; Chen, Y.; Deidda, R.; Haberlandt, U.; Eris, E.; Fischer, S.; Francés, F.; Kavetski, D.; Rodding Kjeldsen, T.; Kochanek, K.; Langousis, A.; Mediero Orduña, L.; Montanari, A.; Nerantzaki, S. D.; Ouarda, T. B.; Prosdocimi, I.; Ragno, E.; Rajulapati, C. R.; Requena, A. I.; Ridolfi, E.; Sadegh, M.; Schumann, A.; Sharma, A. (2024): The legacy of STAHY: Milestones, achievements, challenges, and open problems in statistical hydrology, Hydrological Sciences Journal, 0. https://doi.org/10.1080/02626667.2024.2385686
• Ballarin, A. S.; Vargas Godoy, M. R.; Zaerpour, M.; Abdelmoaty, H. M.; Hatami, S.; Gavasso‐Rita, Y. L.; Wendland, E.; Papalexiou, S. M. (2024): Drought intensification in Brazilian catchments: implications for water and land management, Environmental Research Letters. https://doi.org/10.1088/1748-9326/ad3e18
• Markonis, Y.; Vargas Godoy, M. R.; Pradhan, R. K.; Pratap, S.; Thomson, J. R.; Hanel, M.; Paschalis, A.; Nikolopoulos, E.; Papalexiou, S. M. (2024): Spatial partitioning of terrestrial precipitation reveals varying dataset agreement across different environments, Communications Earth & Environment, 5, 1–10. https://doi.org/10.1038/s43247-024-01377-9
• Abdelmoaty, H. M.; Papalexiou, S. M.; Gaur, A.; Markonis, Y. (2024): Investigating Catchment-Scale Daily Snow Depths of CMIP6 in Canada, Geophysical Research Letters, 51, e2024GL109664. https://doi.org/10.1029/2024GL109664
• Abdelmoaty, H. M.; Papalexiou, S. M.; Nerantzaki, S.; Mascaro, G.; Gaur, A.; Lu, H.; Clark, M. P.; Markonis, Y. (2024): Snow depth time series Generation: Effective simulation at multiple time scales, Journal of Hydrology X, 23, 100177. https://doi.org/10.1016/j.hydroa.2024.100177
• Zaghloul, M. A.; Papalexiou, S. M. (2024): Generation Mechanisms and Probabilistic Assessment of Peak Spring Streamflow in the Canadian Prairies, Stochastic Environmental Research and Risk Assessment, 38, 1071–1088. https://doi.org/10.1007/s00477-023-02614-x
• Vargas Godoy, M. R.; Papalexiou, S. M.; Markonis, Y. (2024): HYADES - A Global Archive of Annual Maxima Daily Precipitation, Scientific Data, 11, 298. https://doi.org/10.1038/s41597-024-03109-2
• Tang, G.; Wood, A. W.; Newman, A. J.; Clark, M. P.; Papalexiou, S. M. (2024): GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications, Geoscientific Model Development, 17, 1153–1173. https://doi.org/10.5194/gmd-17-1153-2024
• Zaerpour, M.; Papalexiou, S. M.; Pietroniro, A.; Nazemi, A. (2024): How extreme are flood peak distributions? A quasi-global analysis of daily discharge records, Journal of Hydrology, 631, 130849. https://doi.org/10.1016/j.jhydrol.2024.130849
• Whitfield, P. H.; Abdelmoaty, H.; Nerantzaki, S.; Papalexiou, S. M. (2024): The 2021 heatwave results in simultaneous but different hydrological responses over Canada west of 100°W, Journal of Hydrology, 130824. https://doi.org/10.1016/j.jhydrol.2024.130824
• Ballarin, A. S.; Wendland, E.; Zaerpour, M.; Hatami, S.; Meira Neto, A. A.; Papalexiou, S. M. (2024): Frequency Rather Than Intensity Drives Projected Changes of Rainfall Events in Brazil, Earth's Future, 12, e2023EF004053. https://doi.org/10.1029/2023EF004053
• Liu, H.; Clark, M. P.; Gharari, S.; Sheikholeslami, R.; Freer, J.; Knoben, W. J. M.; Marsh, C. B.; Papalexiou, S. M. (2024): An Improved Copula-Based Framework for Efficient Global Sensitivity Analysis, Water Resources Research, 60, e2022WR033808. https://doi.org/10.1029/2022WR033808
• Gavasso-Rita, Y. L.; Papalexiou, S. M.; Li, Y.; Elshorbagy, A.; Li, Z.; Schuster-Wallace, C. (2024): Crop models and their use in assessing crop production and food security: A review, Food and Energy Security, 13, e503. https://doi.org/10.1002/fes3.503
• Vargas Godoy, M. R.; Markonis, Y.; Rakovec, O.; Jenicek, M.; Dutta, R.; Pradhan, R. K.; Bešťáková, Z.; Kyselý, J.; Juras, R.; Papalexiou, S. M.; Hanel, M. (2024): Water cycle changes in Czechia: a multi-source water budget perspective, Hydrology and Earth System Sciences, 28, 1–19. https://doi.org/10.5194/hess-28-1-2024
• Rajulapati, C. R.; Tesemma, Z.; Shook, K.; Papalexiou, S. M.; Pomeroy, J. W. (2024): A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections, Journal of Hydrology, 645, 132080. https://doi.org/10.1016/j.jhydrol.2024.132080
• Ballarin, A. S.; Oliveira, P. T.; S. M. Uchôa, J. G. S. M.; Lima, C. H. R.; Zaerpour, M.; Vargas Godoy, M. R.; Meira Neto, A. A.; Papalexiou, S. M.; Wendland, E. (2024): Escalating threat of human-perceived heatwaves in Brazil, Environmental Research Communications. https://doi.org/10.1088/2515-7620/ad9140
• Zaerpour, M.; Hatami, S.; Ballarin, A. S.; Knoben, W. J. M.; Papalexiou, S. M.; Pietroniro, A.; Clark, M. P. (2024): Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain, Communications Earth & Environment, 5, 1–14. https://doi.org/10.1038/s43247-024-01891-w
• Zaerpour, M.; Hatami, S.; Ballarin, A. S.; Papalexiou, S. M.; Pietroniro, A.; Adamowski, J. F. (2024): Climate shapes baseflows, influencing drought severity, Environmental Research Letters, 20, 014035. https://doi.org/10.1088/1748-9326/ad975a

2023

• Zabel, F.; Poschlod, B. (2023): The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis, Geoscientific Model Development, 16, 5383–5399. https://doi.org/10.5194/gmd-16-5383-2023
• Nerantzaki, S. D.; Papalexiou, S. M.; Rajulapati, C. R.; Clark, M. P. (2023): Nonstationarity in High and Low-Temperature Extremes: Insights From a Global Observational Data Set by Merging Extreme-Value Methods, Earth's Future, 11, e2023EF003506. https://doi.org/10.1029/2023EF003506
• Marra, F.; Amponsah, W.; Papalexiou, S. M. (2023): Non-asymptotic Weibull tails explain the statistics of extreme daily precipitation, Advances in Water Resources, 173, 104388. https://doi.org/10.1016/j.advwatres.2023.104388
• Yousfi, N.; El Adlouni, S.; Papalexiou, S. M.; Gachon, P. (2023): Mixture Probability Models with Covariates: Applications in Estimating Risk of Hydroclimatic Extremes, Journal of Hydrologic Engineering, 28, 04023011. https://doi.org/10.1061/JHYEFF.HEENG-5831
• Papalexiou, S. M.; Serinaldi, F.; Clark, M. P. (2023): Large-Domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1,000 Sites, Water Resources Research, 59, e2022WR034094. https://doi.org/10.1029/2022WR034094
• Abdelmoaty, H. M.; Papalexiou, S. M. (2023): Changes of Extreme Precipitation in CMIP6 Projections: Should We Use Stationary or Nonstationary Models?, Journal of Climate, 36, 2999–3014. https://doi.org/10.1175/JCLI-D-22-0467.1
• Rajulapati, C. R.; Papalexiou, S. M. (2023): Precipitation Bias Correction: A Novel Semi-parametric Quantile Mapping Method, Earth and Space Science, 10, e2023EA002823. https://doi.org/10.1029/2023EA002823
• Mascaro, G.; Papalexiou, S. M.; Wright, D. B. (2023): Advancing Characterization and Modeling of Space-Time Correlation Structure and Marginal Distribution of Short-Duration Precipitation, Advances in Water Resources, 177, 104451. https://doi.org/10.1016/j.advwatres.2023.104451
• Tang, G.; Clark, M. P.; Knoben, W. J. M.; Liu, H.; Gharari, S.; Arnal, L.; Beck, H. E.; Wood, A. W.; Newman, A. J.; Papalexiou, S. M. (2023): The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins, Water Resources Research, 59, e2022WR033767. https://doi.org/10.1029/2022WR033767

2022

• Cantonati, M.; Lichtenwöhrer, K.; Leonhardt, G.; Seifert, L.; Mustoni, A.; Hotzy, R.; Schubert, E.; Blattner, L.; Bilous, O.; Lotz, A.; Poschlod, B.; Gerecke, R. (2022): Using Springs as Sentinels of Climate Change in Nature Parks North and South of the Alps: A Critical Evaluation of Methodological Aspects and Recommendations for Long-Term Monitoring, Water, 14, 2843. https://doi.org/10.3390/w14182843
• Poschlod, B. (2022): Attributing heavy rainfall event in Berchtesgadener Land to recent climate change – Further rainfall intensification projected for the future, Weather and Climate Extremes, 38, 100492. https://doi.org/10.1016/j.wace.2022.100492
• Nerantzaki, S. D.; Papalexiou, S. M. (2022): Assessing extremes in hydroclimatology: A review on probabilistic methods, Journal of Hydrology, 605, 127302. https://doi.org/10.1016/j.jhydrol.2021.127302
• Gu, X.; Ye, L.; Xin, Q.; Zhang, C.; Zeng, F.; Nerantzaki, S. D.; Papalexiou, S. M. (2022): Extreme Precipitation in China: A Review on Statistical Methods and Applications, Advances in Water Resources, 163, 104144. https://doi.org/10.1016/j.advwatres.2022.104144
• Pradhan, R. K.; Markonis, Y.; Vargas Godoy, M. R.; Villalba-Pradas, A.; Andreadis, K. M.; Nikolopoulos, E. I.; Papalexiou, S. M.; Rahim, A.; Tapiador, F. J.; Hanel, M. (2022): Review of GPM IMERG performance: A global perspective, Remote Sensing of Environment, 268, 112754. https://doi.org/10.1016/j.rse.2021.112754
• Rajulapati, C. R.; Gaddam, R. K.; Nerantzaki, S. D.; Papalexiou, S. M.; Cannon, A. J.; Clark, M. P. (2022): Exacerbated heat in large Canadian cities, Urban Climate, 42, 101097. https://doi.org/10.1016/j.uclim.2022.101097
• Lipoth, J.; Tereda, Y.; Papalexiou, S. M.; Spiteri, R. J.; Lipoth, J.; Tereda, Y.; Papalexiou, S. M.; Spiteri, R. J. (2022): A new very simply explicitly invertible approximation for the standard normal cumulative distribution function, AIMS Mathematics, 7, 11635–11646. https://doi.org/10.3934/math.2022648
• Rajulapati, C. R.; Abdelmoaty, H. M.; Nerantzaki, S. D.; Papalexiou, S. M. (2022): Changes in the risk of extreme temperatures in megacities worldwide, Climate Risk Management, 36, 100433. https://doi.org/10.1016/j.crm.2022.100433
• Grimaldi, S.; Volpi, E.; Langousis, A.; Papalexiou, S. M.; Luciano De Luca, D.; Piscopia, R.; Nerantzaki, S. D.; Papacharalampous, G.; Petroselli, A. (2022): Continuous hydrologic modelling for small and ungauged basins: A comparison of eight rainfall models for sub-daily runoff simulations, Journal of Hydrology, 610, 127866. https://doi.org/10.1016/j.jhydrol.2022.127866
• Wang, W.; Yin, S.; Gao, G.; Papalexiou, S. M.; Wang, Z. (2022): Increasing trends in rainfall erosivity in the Yellow River basin from 1971 to 2020, Journal of Hydrology, 610, 127851. https://doi.org/10.1016/j.jhydrol.2022.127851
• Papalexiou, S. M. (2022): Rainfall Generation Revisited: Introducing CoSMoS-2s and Advancing Copula-Based Intermittent Time Series Modeling, Water Resources Research, 58, e2021WR031641. https://doi.org/10.1029/2021WR031641
• Hobbi, S.; Papalexiou, S. M.; Rupa Rajulapati, C.; Nerantzaki, S. D.; Markonis, Y.; Tang, G.; Clark, M. P. (2022): Detailed investigation of discrepancies in Köppen-Geiger climate classification using seven global gridded products, Journal of Hydrology, 612, 128121. https://doi.org/10.1016/j.jhydrol.2022.128121
• AghaKouchak, A.; Pan, B.; Mazdiyasni, O.; Sadegh, M.; Jiwa, S.; Zhang, W.; Love, C. A.; Madadgar, S.; Papalexiou, S. M.; Davis, S. J.; Hsu, K.; Sorooshian, S. (2022): Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380, 20210288. https://doi.org/10.1098/rsta.2021.0288
• Tang, G.; Clark, M. P.; Papalexiou, S. M. (2022): EM-Earth: The Ensemble Meteorological Dataset for Planet Earth, Bulletin of the American Meteorological Society, 103, E996–E1018. https://doi.org/10.1175/BAMS-D-21-0106.1
• Schuster-Wallace, C. J.; Dickson-Anderson, S. E.; Papalexiou, S. M.; Ganzouri, A. E. (2022): Design and Application of the Tank Simulation Model (TSM): Assessing the Ability of Rainwater Harvesting to Meet Domestic Water Demand, Journal of Environmental Informatics, 40, 16–29. https://doi.org/doi:10.3808/jei.202200477

2021

• Poschlod, B.; Ludwig, R.; Sillmann, J. (2021): Ten-year return levels of sub-daily extreme precipitation over Europe, Earth System Science Data, 13, 983–1003. https://doi.org/10.5194/essd-13-983-2021
• Poschlod, B.; Ludwig, R. (2021): Internal variability and temperature scaling of future sub-daily rainfall return levels over Europe, Environmental Research Letters, 16, 064097. https://doi.org/10.1088/1748-9326/ac0849
• Santos, V. M.; Casas-Prat, M.; Poschlod, B.; Ragno, E.; Van Den Hurk, B.; Hao, Z.; Kalmár, T.; Zhu, L.; Najafi, H. (2021): Statistical modelling and climate variability of compound surge and precipitation events in a managed water system: a case study in the Netherlands, Hydrology and Earth System Sciences, 25, 3595–3615. https://doi.org/10.5194/hess-25-3595-2021
• Poschlod, B. (2021): Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria, Natural Hazards and Earth System Sciences, 21, 3573–3598. https://doi.org/10.5194/nhess-21-3573-2021
• Abdelmoaty, H. M.; Papalexiou, S. M.; Rajulapati, C. R.; AghaKouchak, A. (2021): Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation, Earth's Future, 9, e2021EF002196. https://doi.org/10.1029/2021EF002196
• Clark, M. P.; Vogel, R. M.; Lamontagne, J. R.; Mizukami, N.; Knoben, W. J. M.; Tang, G.; Gharari, S.; Freer, J. E.; Whitfield, P. H.; Shook, K. R.; Papalexiou, S. M. (2021): The Abuse of Popular Performance Metrics in Hydrologic Modeling, Water Resources Research, 57, e2020WR029001. https://doi.org/10.1029/2020WR029001
• Moccia, B.; Papalexiou, S. M.; Russo, F.; Napolitano, F. (2021): Spatial variability of precipitation extremes over Italy using a fine-resolution gridded product, Journal of Hydrology: Regional Studies, 37, 100906. https://doi.org/10.1016/j.ejrh.2021.100906
• Papalexiou, S. M.; Serinaldi, F.; Porcu, E. (2021): Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond, Water Resources Research, 57, e2020WR029466. https://doi.org/10.1029/2020WR029466
• Tang, G.; Clark, M. P.; Papalexiou, S. M.; Newman, A. J.; Wood, A. W.; Brunet, D.; Whitfield, P. H. (2021): EMDNA: an Ensemble Meteorological Dataset for North America, Earth System Science Data, 13, 3337–3362. https://doi.org/10.5194/essd-13-3337-2021
• Tang, G.; Clark, M. P.; Papalexiou, S. M. (2021): SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019, Journal of Climate, 34, 6493–6511. https://doi.org/10.1175/JCLI-D-21-0067.1
• Sheikholeslami, R.; Gharari, S.; Papalexiou, S. M.; Clark, M. P. (2021): VISCOUS: A Variance-Based Sensitivity Analysis Using Copulas for Efficient Identification of Dominant Hydrological Processes, Water Resources Research, 57, e2020WR028435. https://doi.org/10.1029/2020WR028435
• Moustakis, Y.; Papalexiou, S. M.; Onof, C. J.; Paschalis, A. (2021): Seasonality, Intensity, and Duration of Rainfall Extremes Change in a Warmer Climate, Earth's Future, 9, e2020EF001824. https://doi.org/https://doi.org/10.1029/2020EF001824
• Markonis, Y.; Pappas, C.; Hanel, M.; Papalexiou, S. M. (2021): A cross-scale framework for integrating multi-source data in Earth system sciences, Environmental Modelling & Software, 139, 104997. https://doi.org/10.1016/j.envsoft.2021.104997
• Tyralis, H.; Papacharalampous, G.; Langousis, A.; Papalexiou, S. M. (2021): Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms, Remote Sensing, 13, 333. https://doi.org/10.3390/rs13030333
• Shook, K.; Papalexiou, S. M.; Pomeroy, J. W. (2021): Quantifying the effects of Prairie depressional storage complexes on drainage basin connectivity, Journal of Hydrology, 593, 125846. https://doi.org/10.1016/j.jhydrol.2020.125846
• Zaerpour, M.; Papalexiou, S. M.; Nazemi, A. (2021): Informing Stochastic Streamflow Generation by Large-Scale Climate Indices at Single and Multiple Sites, Advances in Water Resources, 156, 104037. https://doi.org/10.1016/j.advwatres.2021.104037
• Vargas Godoy, M. R.; Markonis, Y.; Hanel, M.; Kyselý, J.; Papalexiou, S. M. (2021): The Global Water Cycle Budget: A Chronological Review, Surveys in Geophysics, 42, 1075–1107. https://doi.org/10.1007/s10712-021-09652-6
• Tang, G.; Clark, M. P.; Papalexiou, S. M. (2021): The Use of Serially Complete Station Data to Improve the Temporal Continuity of Gridded Precipitation and Temperature Estimates, Journal of Hydrometeorology, 22, 1553–1568. https://doi.org/10.1175/JHM-D-20-0313.1
• Rajulapati, C. R.; Papalexiou, S. M.; Clark, M. P.; Pomeroy, J. W. (2021): The Perils of Regridding: Examples Using a Global Precipitation Dataset, Journal of Applied Meteorology and Climatology, 60, 1561–1573. https://doi.org/10.1175/JAMC-D-20-0259.1
• Papalexiou, S. M.; Rajulapati, C. R.; Andreadis, K. M.; Foufoula-Georgiou, E.; Clark, M. P.; Trenberth, K. E. (2021): Probabilistic Evaluation of Drought in CMIP6 Simulations, Earth's Future, 9, e2021EF002150. https://doi.org/10.1029/2021EF002150
• Papacharalampous, G.; Tyralis, H.; Papalexiou, S. M.; Langousis, A.; Khatami, S.; Volpi, E.; Grimaldi, S. (2021): Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity, Science of The Total Environment, 767, 144612. https://doi.org/10.1016/j.scitotenv.2020.144612

2020

• Bueche, T.; Wenk, M.; Poschlod, B.; Giadrossich, F.; Pirastru, M.; Vetter, M. (2020): glmGUI v1.0: an R-based graphical user interface and toolbox for GLM (General Lake Model) simulations, Geoscientific Model Development, 13, 565–580. https://doi.org/10.5194/gmd-13-565-2020
• Poschlod, B.; Zscheischler, J.; Sillmann, J.; Wood, R. R.; Ludwig, R. (2020): Climate change effects on hydrometeorological compound events over southern Norway, Weather and Climate Extremes, 28, 100253. https://doi.org/10.1016/j.wace.2020.100253
• Poschlod, B.; Willkofer, F.; Ludwig, R. (2020): Impact of Climate Change on the Hydrological Regimes in Bavaria, Water, 12, 1599. https://doi.org/10.3390/w12061599
• Willkofer, F.; Wood, R. R.; Von Trentini, F.; Weismüller, J.; Poschlod, B.; Ludwig, R. (2020): A Holistic Modelling Approach for the Estimation of Return Levels of Peak Flows in Bavaria, Water, 12, 2349. https://doi.org/10.3390/w12092349
• Papalexiou, S. M.; Serinaldi, F. (2020): Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity, Water Resources Research, 56, e2019WR026331. https://doi.org/10.1029/2019WR026331
• Tang, G.; Clark, M. P.; Papalexiou, S. M.; Ma, Z.; Hong, Y. (2020): Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets, Remote Sensing of Environment, 240, 111697. https://doi.org/10.1016/j.rse.2020.111697
• Rajulapati, C. R.; Papalexiou, S. M.; Clark, M. P.; Razavi, S.; Tang, G.; Pomeroy, J. W. (2020): Assessment of Extremes in Global Precipitation Products: How Reliable Are They?, Journal of Hydrometeorology, 21, 2855–2873. https://doi.org/10.1175/JHM-D-20-0040.1
• Zaghloul, M.; Papalexiou, S. M.; Elshorbagy, A.; Coulibaly, P. (2020): Revisiting flood peak distributions: A pan-Canadian investigation, Advances in Water Resources, 145, 103720. https://doi.org/10.1016/j.advwatres.2020.103720
• Brunner, M. I.; Papalexiou, S. M.; Clark, M. P.; Gilleland, E. (2020): How Probable Is Widespread Flooding in the United States?, Water Resources Research, 56, e2020WR028096. https://doi.org/10.1029/2020WR028096
• Papalexiou, S. M.; Rajulapati, C. R.; Clark, M. P.; Lehner, F. (2020): Robustness of CMIP6 Historical Global Mean Temperature Simulations: Trends, Long-Term Persistence, Autocorrelation, and Distributional Shape, Earth's Future, 8, e2020EF001667. https://doi.org/10.1029/2020EF001667
• Tang, G.; Clark, M. P.; Newman, A. J.; Wood, A. W.; Papalexiou, S. M.; Vionnet, V.; Whitfield, P. H. (2020): SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018, Earth System Science Data, 12, 2381–2409. https://doi.org/https://doi.org/10.5194/essd-12-2381-2020
• Salas, J. D.; Anderson, M. L.; Papalexiou, S. M.; Frances, F. (2020): PMP and Climate Variability and Change: A Review, Journal of Hydrologic Engineering, 25, 03120002. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002003
• AghaKouchak, A.; Chiang, F.; Huning, L. S.; Love, C. A.; Mallakpour, I.; Mazdiyasni, O.; Moftakhari, H.; Papalexiou, S. M.; Ragno, E.; Sadegh, M. (2020): Climate Extremes and Compound Hazards in a Warming World, Annual Review of Earth and Planetary Sciences, 48, 519–548. https://doi.org/10.1146/annurev-earth-071719-055228

2019

• Papalexiou, S. M.; Montanari, A. (2019): Global and Regional Increase of Precipitation Extremes Under Global Warming, Water Resources Research, 55, 4901–4914. https://doi.org/https://doi.org/10.1029/2018WR024067
• Nerantzaki, S. D.; Papalexiou, S. M. (2019): Tails of extremes: Advancing a graphical method and harnessing big data to assess precipitation extremes, Advances in Water Resources, 134, 103448. https://doi.org/10.1016/j.advwatres.2019.103448
• Markonis, Y.; Papalexiou, S. M.; Martinkova, M.; Hanel, M. (2019): Assessment of Water Cycle Intensification Over Land using a Multisource Global Gridded Precipitation DataSet, Journal of Geophysical Research: Atmospheres, 124, 11175–11187. https://doi.org/10.1029/2019JD030855

2018

• Poschlod, B.; Hodnebrog, Ø.; Wood, R. R.; Alterskjær, K.; Ludwig, R.; Myhre, G.; Sillmann, J. (2018): Comparison and Evaluation of Statistical Rainfall Disaggregation and High-Resolution Dynamical Downscaling over Complex Terrain, Journal of Hydrometeorology, 19, 1973–1982. https://doi.org/10.1175/JHM-D-18-0132.1
• Papalexiou, S. M.; Markonis, Y.; Lombardo, F.; AghaKouchak, A.; Foufoula‐Georgiou, E. (2018): Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes, Water Resources Research, 54, 7435–7458. https://doi.org/10.1029/2018WR022726
• Tsoukalas, I.; Papalexiou, S. M.; Efstratiadis, A.; Makropoulos, C. (2018): A Cautionary Note on the Reproduction of Dependencies through Linear Stochastic Models with Non-Gaussian White Noise, Water, 10, 771. https://doi.org/10.3390/w10060771
• Papalexiou, S. M.; AghaKouchak, A.; Trenberth, K. E.; Foufoula‐Georgiou, E. (2018): Global, Regional, and Megacity Trends in the Highest Temperature of the Year: Diagnostics and Evidence for Accelerating Trends, Earth's Future, 6, 71–79. https://doi.org/10.1002/2017EF000709
• Papaioannou, G.; Efstratiadis, A.; Vasiliades, L.; Loukas, A.; Papalexiou, S. M.; Koukouvinos, A.; Tsoukalas, I.; Kossieris, P. (2018): An Operational Method for Flood Directive Implementation in Ungauged Urban Areas, Hydrology, 5, 24. https://doi.org/10.3390/hydrology5020024
• Markonis, Y.; Moustakis, Y.; Nasika, C.; Sychova, P.; Dimitriadis, P.; Hanel, M.; Máca, P.; Papalexiou, S. M. (2018): Global estimation of long-term persistence in annual river runoff, Advances in Water Resources, 113, 1–12. https://doi.org/10.1016/j.advwatres.2018.01.003
• Iliopoulou, T.; Papalexiou, S. M.; Markonis, Y.; Koutsoyiannis, D. (2018): Revisiting long-range dependence in annual precipitation, Journal of Hydrology, 556, 891–900. https://doi.org/10.1016/j.jhydrol.2016.04.015
• Papalexiou, S. M.; AghaKouchak, A.; Foufoula-Georgiou, E. (2018): A Diagnostic Framework for Understanding Climatology of Tails of Hourly Precipitation Extremes in the United States, Water Resources Research, 54, 6725–6738. https://doi.org/10.1029/2018WR022732
• Papalexiou, S. M. (2018): Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency, Advances in Water Resources, 115, 234–252. https://doi.org/10.1016/j.advwatres.2018.02.013

2016

• Papalexiou, S. M.; Koutsoyiannis, D. (2016): A global survey on the seasonal variation of the marginal distribution of daily precipitation, Advances in Water Resources, 94, 131–145. https://doi.org/10.1016/j.advwatres.2016.05.005
• Papalexiou, S. M.; Dialynas, Y. G.; Grimaldi, S. (2016): Hershfield factor revisited: Correcting annual maximum precipitation, Journal of Hydrology, 542, 884–895. https://doi.org/10.1016/j.jhydrol.2016.09.058

2014

• Lombardo, F.; Volpi, E.; Koutsoyiannis, D.; Papalexiou, S. M. (2014): Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology, Hydrology and Earth System Sciences, 18, 243–255. https://doi.org/https://doi.org/10.5194/hess-18-243-2014
• Pappas, C.; Papalexiou, S. M.; Koutsoyiannis, D. (2014): A quick gap filling of missing hydrometeorological data, Journal of Geophysical Research: Atmospheres, 119, 9290–9300. https://doi.org/10.1002/2014JD021633

2013

• Papalexiou, S. M.; Koutsoyiannis, D. (2013): Battle of extreme value distributions: A global survey on extreme daily rainfall, Water Resources Research, 49, 187–201. https://doi.org/10.1029/2012WR012557
• Papalexiou, S. M.; Koutsoyiannis, D.; Makropoulos, C. (2013): How extreme is extreme? An assessment of daily rainfall distribution tails, Hydrology and Earth System Sciences, 17, 851–862. https://doi.org/10.5194/hess-17-851-2013

2012

• Papalexiou, S. M.; Koutsoyiannis, D. (2012): Entropy based derivation of probability distributions: A case study to daily rainfall, Advances in Water Resources, 45, 51–57. https://doi.org/10.1016/j.advwatres.2011.11.007

2011

• Papalexiou, S. M.; Koutsoyiannis, D.; Montanari, A. (2011): Can a simple stochastic model generate rich patterns of rainfall events?, Journal of Hydrology, 411, 279–289. https://doi.org/10.1016/j.jhydrol.2011.10.008

2006

• Papalexiou, S. M.; Koutsoyiannis, D. (2006): A probabilistic approach to the concept of Probable Maximum Precipitation, Advances in Geosciences, 7, 51–54.