Articles | Volume 29, issue 14
https://doi.org/10.5194/hess-29-3073-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3073-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya
British Antarctic Survey, UK Research and Innovation, Cambridge, UK
Department of Engineering, University of Cambridge, Cambridge, UK
Andrew Orr
British Antarctic Survey, UK Research and Innovation, Cambridge, UK
Martin Widmann
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
Daniel Bannister
WTW Research Network, WTW, London, UK
Ghulam Hussain Dars
U.S.-Pakistan Center for Advanced Studies in Water, Mehran University of Engineering and Technology, Jamshoro, Pakistan
Scott Hosking
British Antarctic Survey, UK Research and Innovation, Cambridge, UK
The Alan Turing Institute, London, UK
Jesse Norris
Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, CA, USA
David Ocio
Mott MacDonald, Cambridge, UK
Tony Phillips
British Antarctic Survey, UK Research and Innovation, Cambridge, UK
Jakob Steiner
Institute of Geography and Regional Science, University of Graz, Graz, Austria
Himalayan University Consortium, Lalitpur, Nepal
Richard E. Turner
Department of Engineering, University of Cambridge, Cambridge, UK
Related authors
No articles found.
Francesca Pellicciotti, Adrià Fontrodona-Bach, David R. Rounce, Catriona L. Fyffe, Leif S. Anderson, Álvaro Ayala, Ben W. Brock, Pascal Buri, Stefan Fugger, Koji Fujita, Prateek Gantayat, Alexander R. Groos, Walter Immerzeel, Marin Kneib, Christoph Mayer, Shelley MacDonell, Michael McCarthy, James McPhee, Evan Miles, Heather Purdie, Ekaterina Rets, Akiko Sakai, Thomas E. Shaw, Jakob Steiner, Patrick Wagnon, and Alex Winter-Billington
EGUsphere, https://doi.org/10.5194/egusphere-2025-3837, https://doi.org/10.5194/egusphere-2025-3837, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Rock debris covers many of the world glaciers, modifying the transfer of atmospheric energy to the debris and into the ice. Models of different complexity simulate this process, and we compare 14 models at 9 sites to show that the most complex models at the debris-atmosphere interface have the highest performance. However, we lack debris properties and their derivation from measurements is ambiguous, hindering global modelling and calling for both model development and data collection.
Jakob Steiner, William Armstrong, Will Kochtitzky, Robert McNabb, Rodrigo Aguayo, Tobias Bolch, Fabien Maussion, Vibhor Agarwal, Iestyn Barr, Nathaniel R. Baurley, Mike Cloutier, Katelyn DeWater, Frank Donachie, Yoann Drocourt, Siddhi Garg, Gunjan Joshi, Byron Guzman, Stanislav Kutuzov, Thomas Loriaux, Caleb Mathias, Biran Menounos, Evan Miles, Aleksandra Osika, Kaleigh Potter, Adina Racoviteanu, Brianna Rick, Miles Sterner, Guy D. Tallentire, Levan Tielidze, Rebecca White, Kunpeng Wu, and Whyjay Zheng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-315, https://doi.org/10.5194/essd-2025-315, 2025
Preprint under review for ESSD
Short summary
Short summary
Many mountain glaciers around the world flow into lakes – exactly how many however, has never been mapped. Across a large team of experts we have now identified all glaciers that end in lakes. Only about 1% do so, but they are generally larger than those which end on land. This is important to understand, as lakes can influence the behaviour of glacier ice, including how fast it disappears. This new dataset allows us to better model glaciers at a global scale, accounting for the effect of lakes.
Jakob Steiner, Jakob Abermann, and Rainer Prinz
EGUsphere, https://doi.org/10.5194/egusphere-2025-2424, https://doi.org/10.5194/egusphere-2025-2424, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Ice in Greenland either ends in the ocean or on land and in lakes. We show that more than 95% of the margin ends on land. Ice ending in lakes is much rarer, but with 1.4% quite similar to the 2.2% ending in oceans. We also see that more than 20% of the margin ends in extremely steep, often vertical cliffs. We will now be able to compare these maps against local ice velocities, mass loss and climate to understand whether the margin shape teaches us something about the health of ice in the region.
Ella Gilbert, Denis Pishniak, José Abraham Torres, Andrew Orr, Michelle Maclennan, Nander Wever, and Kristiina Verro
The Cryosphere, 19, 597–618, https://doi.org/10.5194/tc-19-597-2025, https://doi.org/10.5194/tc-19-597-2025, 2025
Short summary
Short summary
We use three sophisticated climate models to examine extreme precipitation in a critical region of West Antarctica. We found that rainfall probably occurred during the two cases we examined and that it was generated by the interaction of air with steep topography. Our results show that kilometre-scale models are useful tools for exploring extreme precipitation in this region and that more observations of rainfall are needed.
Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner
Hydrol. Earth Syst. Sci., 28, 4903–4925, https://doi.org/10.5194/hess-28-4903-2024, https://doi.org/10.5194/hess-28-4903-2024, 2024
Short summary
Short summary
This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.
Xavier J. Levine, Ryan S. Williams, Gareth Marshall, Andrew Orr, Lise Seland Graff, Dörthe Handorf, Alexey Karpechko, Raphael Köhler, René R. Wijngaard, Nadine Johnston, Hanna Lee, Lars Nieradzik, and Priscilla A. Mooney
Earth Syst. Dynam., 15, 1161–1177, https://doi.org/10.5194/esd-15-1161-2024, https://doi.org/10.5194/esd-15-1161-2024, 2024
Short summary
Short summary
While the most recent climate projections agree that the Arctic is warming, differences remain in how much and in other climate variables such as precipitation. This presents a challenge for stakeholders who need to develop mitigation and adaptation strategies. We tackle this problem by using the storyline approach to generate four plausible and actionable realisations of end-of-century climate change for the Arctic, spanning its most likely range of variability.
Nicolaj Hansen, Andrew Orr, Xun Zou, Fredrik Boberg, Thomas J. Bracegirdle, Ella Gilbert, Peter L. Langen, Matthew A. Lazzara, Ruth Mottram, Tony Phillips, Ruth Price, Sebastian B. Simonsen, and Stuart Webster
The Cryosphere, 18, 2897–2916, https://doi.org/10.5194/tc-18-2897-2024, https://doi.org/10.5194/tc-18-2897-2024, 2024
Short summary
Short summary
We investigated a melt event over the Ross Ice Shelf. We use regional climate models and a firn model to simulate the melt and compare the results with satellite data. We find that the firn model aligned well with observed melt days in certain parts of the ice shelf. The firn model had challenges accurately simulating the melt extent in the western sector. We identified potential reasons for these discrepancies, pointing to limitations in the models related to representing the cloud properties.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
Short summary
Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Matthew D. K. Priestley, David B. Stephenson, Adam A. Scaife, Daniel Bannister, Christopher J. T. Allen, and David Wilkie
Nat. Hazards Earth Syst. Sci., 23, 3845–3861, https://doi.org/10.5194/nhess-23-3845-2023, https://doi.org/10.5194/nhess-23-3845-2023, 2023
Short summary
Short summary
This research presents a model for estimating extreme gusts associated with European windstorms. Using observed storm footprints we are able to calculate the return level of events at the 200-year return period. The largest gusts are found across NW Europe, and these are larger when the North Atlantic Oscillation is positive. Using theoretical future climate states we find that return levels are likely to increase across NW Europe to levels that are unprecedented compared to historical storms.
Finu Shrestha, Jakob F. Steiner, Reeju Shrestha, Yathartha Dhungel, Sharad P. Joshi, Sam Inglis, Arshad Ashraf, Sher Wali, Khwaja M. Walizada, and Taigang Zhang
Earth Syst. Sci. Data, 15, 3941–3961, https://doi.org/10.5194/essd-15-3941-2023, https://doi.org/10.5194/essd-15-3941-2023, 2023
Short summary
Short summary
A new inventory of glacial lake outburst floods (GLOFs) in High Mountain Asia found 697 events, causing 906 deaths, 3 times more than previously reported. This study provides insights into the contributing factors behind GLOFs on a regional scale and highlights the need for interdisciplinary approaches, including scientific communities and local knowledge, to understand GLOF risks in Asia. This study allows integration with other datasets, enabling future local and regional risk assessments.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
Short summary
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Anushilan Acharya, Jakob F. Steiner, Khwaja Momin Walizada, Salar Ali, Zakir Hussain Zakir, Arnaud Caiserman, and Teiji Watanabe
Nat. Hazards Earth Syst. Sci., 23, 2569–2592, https://doi.org/10.5194/nhess-23-2569-2023, https://doi.org/10.5194/nhess-23-2569-2023, 2023
Short summary
Short summary
All accessible snow and ice avalanches together with previous scientific research, local knowledge, and existing or previously active adaptation and mitigation solutions were investigated in the high mountain Asia (HMA) region to have a detailed overview of the state of knowledge and identify gaps. A comprehensive avalanche database from 1972–2022 is generated, including 681 individual events. The database provides a basis for the forecasting of avalanche hazards in different parts of HMA.
Colin Manning, Martin Widmann, Douglas Maraun, Anne F. Van Loon, and Emanuele Bevacqua
Weather Clim. Dynam., 4, 309–329, https://doi.org/10.5194/wcd-4-309-2023, https://doi.org/10.5194/wcd-4-309-2023, 2023
Short summary
Short summary
Climate models differ in their representation of dry spells and high temperatures, linked to errors in the simulation of persistent large-scale anticyclones. Models that simulate more persistent anticyclones simulate longer and hotter dry spells, and vice versa. This information is important to consider when assessing the likelihood of such events in current and future climate simulations so that we can assess the plausibility of their future projections.
Jeremy Carter, Amber Leeson, Andrew Orr, Christoph Kittel, and J. Melchior van Wessem
The Cryosphere, 16, 3815–3841, https://doi.org/10.5194/tc-16-3815-2022, https://doi.org/10.5194/tc-16-3815-2022, 2022
Short summary
Short summary
Climate models provide valuable information for studying processes such as the collapse of ice shelves over Antarctica which impact estimates of sea level rise. This paper examines variability across climate simulations over Antarctica for fields including snowfall, temperature and melt. Significant systematic differences between outputs are found, occurring at both large and fine spatial scales across Antarctica. Results are important for future impact assessments and model development.
Adam Emmer, Simon K. Allen, Mark Carey, Holger Frey, Christian Huggel, Oliver Korup, Martin Mergili, Ashim Sattar, Georg Veh, Thomas Y. Chen, Simon J. Cook, Mariana Correas-Gonzalez, Soumik Das, Alejandro Diaz Moreno, Fabian Drenkhan, Melanie Fischer, Walter W. Immerzeel, Eñaut Izagirre, Ramesh Chandra Joshi, Ioannis Kougkoulos, Riamsara Kuyakanon Knapp, Dongfeng Li, Ulfat Majeed, Stephanie Matti, Holly Moulton, Faezeh Nick, Valentine Piroton, Irfan Rashid, Masoom Reza, Anderson Ribeiro de Figueiredo, Christian Riveros, Finu Shrestha, Milan Shrestha, Jakob Steiner, Noah Walker-Crawford, Joanne L. Wood, and Jacob C. Yde
Nat. Hazards Earth Syst. Sci., 22, 3041–3061, https://doi.org/10.5194/nhess-22-3041-2022, https://doi.org/10.5194/nhess-22-3041-2022, 2022
Short summary
Short summary
Glacial lake outburst floods (GLOFs) have attracted increased research attention recently. In this work, we review GLOF research papers published between 2017 and 2021 and complement the analysis with research community insights gained from the 2021 GLOF conference we organized. The transdisciplinary character of the conference together with broad geographical coverage allowed us to identify progress, trends and challenges in GLOF research and outline future research needs and directions.
Nicolaj Hansen, Sebastian B. Simonsen, Fredrik Boberg, Christoph Kittel, Andrew Orr, Niels Souverijns, J. Melchior van Wessem, and Ruth Mottram
The Cryosphere, 16, 711–718, https://doi.org/10.5194/tc-16-711-2022, https://doi.org/10.5194/tc-16-711-2022, 2022
Short summary
Short summary
We investigate the impact of different ice masks when modelling surface mass balance over Antarctica. We used ice masks and data from five of the most used regional climate models and a common mask. We see large disagreement between the ice masks, which has a large impact on the surface mass balance, especially around the Antarctic Peninsula and some of the largest glaciers. We suggest a solution for creating a new, up-to-date, high-resolution ice mask that can be used in Antarctic modelling.
Anna Vaughan, Will Tebbutt, J. Scott Hosking, and Richard E. Turner
Geosci. Model Dev., 15, 251–268, https://doi.org/10.5194/gmd-15-251-2022, https://doi.org/10.5194/gmd-15-251-2022, 2022
Short summary
Short summary
We develop a new method for climate downscaling, i.e. transforming low-resolution climate model output to high-resolution projections, using a deep-learning model known as a convolutional conditional neural process. This model is shown to outperform an ensemble of baseline methods for downscaling daily maximum temperature and precipitation and provides a powerful new downscaling framework for climate impact studies.
Ruth Mottram, Nicolaj Hansen, Christoph Kittel, J. Melchior van Wessem, Cécile Agosta, Charles Amory, Fredrik Boberg, Willem Jan van de Berg, Xavier Fettweis, Alexandra Gossart, Nicole P. M. van Lipzig, Erik van Meijgaard, Andrew Orr, Tony Phillips, Stuart Webster, Sebastian B. Simonsen, and Niels Souverijns
The Cryosphere, 15, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021, https://doi.org/10.5194/tc-15-3751-2021, 2021
Short summary
Short summary
We compare the calculated surface mass budget (SMB) of Antarctica in five different regional climate models. On average ~ 2000 Gt of snow accumulates annually, but different models vary by ~ 10 %, a difference equivalent to ± 0.5 mm of global sea level rise. All models reproduce observed weather, but there are large differences in regional patterns of snowfall, especially in areas with very few observations, giving greater uncertainty in Antarctic mass budget than previously identified.
Maurice van Tiggelen, Paul C. J. P. Smeets, Carleen H. Reijmer, Bert Wouters, Jakob F. Steiner, Emile J. Nieuwstraten, Walter W. Immerzeel, and Michiel R. van den Broeke
The Cryosphere, 15, 2601–2621, https://doi.org/10.5194/tc-15-2601-2021, https://doi.org/10.5194/tc-15-2601-2021, 2021
Short summary
Short summary
We developed a method to estimate the aerodynamic properties of the Greenland Ice Sheet surface using either UAV or ICESat-2 elevation data. We show that this new method is able to reproduce the important spatiotemporal variability in surface aerodynamic roughness, measured by the field observations. The new maps of surface roughness can be used in atmospheric models to improve simulations of surface turbulent heat fluxes and therefore surface energy and mass balance over rough ice worldwide.
Andrew Orr, Hua Lu, Patrick Martineau, Edwin P. Gerber, Gareth J. Marshall, and Thomas J. Bracegirdle
Atmos. Chem. Phys., 21, 7451–7472, https://doi.org/10.5194/acp-21-7451-2021, https://doi.org/10.5194/acp-21-7451-2021, 2021
Short summary
Short summary
Reanalysis datasets combine observations and weather forecast simulations to create our best estimate of the state of the atmosphere and are important for climate monitoring. Differences in the technical details of these products mean that they may give different results. This study therefore examined how changes associated with the so-called Antarctic ozone hole are represented, which is one of the most important climate changes in recent decades, and showed that they were broadly consistent.
Andrew Orr, J. Scott Hosking, Aymeric Delon, Lars Hoffmann, Reinhold Spang, Tracy Moffat-Griffin, James Keeble, Nathan Luke Abraham, and Peter Braesicke
Atmos. Chem. Phys., 20, 12483–12497, https://doi.org/10.5194/acp-20-12483-2020, https://doi.org/10.5194/acp-20-12483-2020, 2020
Short summary
Short summary
Polar stratospheric clouds (PSCs) are clouds found in the Antarctic winter stratosphere and are implicated in the formation of the ozone hole. These clouds can sometimes be formed or enhanced by mountain waves, formed as air passes over hills or mountains. However, this important mechanism is missing in coarse-resolution climate models, limiting our ability to simulate ozone. This study examines an attempt to include the effects of mountain waves and their impact on PSCs and ozone.
Cited articles
Andermann, C., Bonnet, S., and Gloaguen, R.: Evaluation of precipitation data sets along the Himalayan front, Geochem. Geophy. Geosy., 12, Q07023, https://doi.org/10.1029/2011GC003513, 2011. a, b
Angus, M., Widmann, M., Orr, A., Ashrit, R., Leckebusch, G. C., and Mitra, A.: A comparison of two statistical postprocessing methods for heavy-precipitation forecasts over India during the summer monsoon, Q. J. Roy. Meteor. Soc., 150, 1865–1883, https://doi.org/10.1002/qj.4677, 2024. a, b, c
Archer, D. R. and Fowler, H. J.: Spatial and temporal variations in precipitation in the Upper Indus Basin, global teleconnections and hydrological implications, Hydrol. Earth Syst. Sci., 8, 47–61, https://doi.org/10.5194/hess-8-47-2004, 2004. a
Arfan, M., Lund, J., Hassan, D., Saleem, M., and Ahmad, A.: Assessment of spatial and temporal flow variability of the Indus River, Resources, 8, 103, https://doi.org/10.3390/resources8020103, 2019. a
Bannister, D., Orr, A., Jain, S. K., Holman, I. P., Momblanch, A., Phillips, T., Adeloye, A. J., Snapir, B., Waine, T. W., Hosking, J. S., and Allen-Sader, C.: Bias Correction of High-Resolution Regional Climate Model Precipitation Output Gives the Best Estimates of Precipitation in Himalayan Catchments, J. Geophys. Res.-Atmos., 124, 14220–14239, https://doi.org/10.1029/2019JD030804, 2019. a, b, c, d, e, f, g, h, i
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020. a, b
Baudouin, J. P., Herzog, M., and Petrie, C. A.: Contribution of cross-barrier moisture transport to precipitation in the upper indus river basin, Mon. Weather Rev., 148, 2801–2818, https://doi.org/10.1175/MWR-D-19-0384.1, 2020. a, b
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Dijk, A. I. V., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-hourly 0.1° Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019. a, b
Bhardwaj, A., Wasson, R. J., Chow, W. T., and Ziegler, A. D.: High-intensity monsoon rainfall variability and its attributes: a case study for Upper Ganges Catchment in the Indian Himalaya during 1901–2013, Nat. Hazards, 105, 2907–2936, https://doi.org/10.1007/s11069-020-04431-9, 2021. a, b
Bolch, T., Kulkarni, A. V., Kääb, A., Huggel, C., Paul, F., Cogley, J. G., Frey, H., Kargel, J. S., Fujita, K., Scheel, M., Bajracharya, S., and Stoffel, M.: The state and fate of Himalayan glaciers., Science, 336, 310–314, https://doi.org/10.1126/science.1215828, 2012. a
Bookhagen, B. and Burbank, D. W.: Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge, J. Geophys. Res.-Earth, 115, F03019, https://doi.org/10.1029/2009JF001426, 2010. a, b, c
Burt, D. R., Shen, Y., and Broderick, T.: Consistent Validation for Predictive Methods in Spatial Settings, arXiv [preprint], https://doi.org/10.48550/arXiv.2402.03527, 2024. a
Cavanaugh, J. E.: A large-sample model selection criterion based on Kullback's symmetric divergence, Stat. Prob. Lett., 42, 333–343, https://doi.org/10.1016/S0167-7152(98)00200-4, 1999. a, b
Chinnasamy, P., Bharati, L., Bhattarai, U., Khadka, A., Dahal, V., and Wahid, S.: Impact of planned water resource development on current and future water demand in the Koshi River basin, Nepal, Water Int., 40, 1004–1020, https://doi.org/10.1080/02508060.2015.1099192, 2015. a
Clark, M., Hay, L., Rajagopalan, B., and Wilby, R.: The Schaake Shuffle: A Method for Reconstructing Space-Time Variability in Forecasted Precipitation and Temperature Fields, Tech. Rep., J. Hydrometeorol., 5, 243–262, 2004. a
Das, S., Ashrit, R., and Moncrieff, M. W.: Simulation of a Himalayan cloudburst event, J. Earth Syst. Sci., 115, 299–313, https://doi.org/10.1007/BF02702044, 2006. a
Dimri, A. P.: Bias correction demonstration in two of the Indian Himalayan river basins, J. Water Clim. Change, 12, 1297–1309, https://doi.org/10.2166/wcc.2020.119, 2021. a
Dimri, A. P., Niyogi, D., Barros, A. P., Ridley, J., Mohanty, U. C., Yasunari, T., and Sikka, D. R.: Western Disturbances: A review, Rev. Geophys., 53, 225–246, https://doi.org/10.1002/2014RG000460, 2015. a, b
Eden, J. M., Widmann, M., Grawe, D., and Rast, S.: Skill, correction, and downscaling of GCM-simulated precipitation, J. Climate, 25, 3970–3984, https://doi.org/10.1175/JCLI-D-11-00254.1, 2012. a
Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D. J., Eslami, S. M. A., and Teh, Y. W.: Neural Processes, arXiv [preprint], https://doi.org/10.48550/arXiv.1807.01622, 2018. a
Giorgi, F.: Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next?, J. Geophys. Res.-Atmos., 124, 5696–5723, https://doi.org/10.1029/2018JD030094, 2019. a
Girona-Mata, M.: mgironamata/pddp-mountains: v0.1.0 (v0.1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.15903332, 2025. a
Gneiting, T. and Raftery, A. E.: Strictly proper scoring rules, prediction, and estimation, J. Am. Stat. Assoc., 102, 359–378, https://doi.org/10.1198/016214506000001437, 2007. a, b, c
Gneiting, T. and Ranjan, R.: Comparing density forecasts using threshold and quantile-weighted scoring rules, J. Bus. Econ. Stat., 29, 411–422, https://doi.org/10.1198/jbes.2010.08110, 2011. a
Haslinger, K., Breinl, K., Pavlin, L., Pistotnik, G., Bertola, M., Olefs, M., Greilinger, M., Schöner, W., and Blöschl, G.: Increasing hourly heavy rainfall in Austria reflected in flood changes, Nature, 639, 667–672, https://doi.org/10.1038/s41586-025-08647-2, 2025. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Huang, Y., Bárdossy, A., and Zhang, K.: Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data, Hydrol. Earth Syst. Sci., 23, 2647–2663, https://doi.org/10.5194/hess-23-2647-2019, 2019. a
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007. a
Hunt, K. M. and Dimri, A. P.: Synoptic-scale precursors of landslides in the western Himalaya and Karakoram, Sci. Total Environ., 776, 145895, https://doi.org/10.1016/j.scitotenv.2021.145895, 2021. a
Immerzeel, W. W., van Beek, L. P. H., and Bierkens, M. F. P.: Climate change will affect the Asian water towers., Science, 328, 1382–1385, https://doi.org/10.1126/science.1183188, 2010. a
Immerzeel, W. W., Petersen, L., Ragettli, S., and Pellicciotti, F.: The importance of observed gradients of air temperature and precipitation for modeling runoff from a glacierized watershed in the Nepalese Himalayas, Water Resour. Res., 50, 2212–2226, https://doi.org/10.1002/2013WR014506, 2014. a
Immerzeel, W. W., Wanders, N., Lutz, A. F., Shea, J. M., and Bierkens, M. F. P.: Reconciling high-altitude precipitation in the upper Indus basin with glacier mass balances and runoff, Hydrol. Earth Syst. Sci., 19, 4673–4687, https://doi.org/10.5194/hess-19-4673-2015, 2015. a, b
Iqbal, Z., Shahid, S., Ahmed, K., Ismail, T., and Nawaz, N.: Spatial distribution of the trends in precipitation and precipitation extremes in the sub-Himalayan region of Pakistan, Theor. Appl. Climatol., 137, 2755–2769, https://doi.org/10.1007/s00704-019-02773-4, 2019. a
Ji, X., Li, Y., Luo, X., He, D., Guo, R., Wang, J., Bai, Y., Yue, C., and Liu, C.: Evaluation of bias correction methods for APHRODITE data to improve hydrologic simulation in a large Himalayan basin, Atmos. Res., 242, 104964, https://doi.org/10.1016/j.atmosres.2020.104964, 2020. a, b
Jones, R. and Mearns, L.: Assessing future climate risks. Adaptation policy frameworks for climate change: Developing strategies, policies and measures, Tech. Rep., Cambridge University Press, Cambridge, UK, 119–143, 2005. a
Kingma, D. P. and Ba, J. L.: Adam: A method for stochastic optimization, in: 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proceedings, 7–9 May 2015, San Diego, California, 2015. a
Klein, W. H. and Glahn, H. R.: Forecasting Local Weather by Means of Model Output Statistics, B. Am. Meteorol. Soc., 55, 1217–1227, https://doi.org/10.1175/1520-0477(1974)055<1217:flwbmo>2.0.co;2, 1974. a
Kokhlikyan, N., Miglani, V., Martin, M., Wang, E., Alsallakh, B., Reynolds, J., Melnikov, A., Kliushkina, N., Araya, C., Yan, S., and Reblitz-Richardson, O.: Captum: A unified and generic model interpretability library for PyTorch, Tech. Rep., arXiv, arXiv:2009.07896, https://arxiv.org/abs/2009.07896 (last access: 1 September ), 2020. a
Krishnan, R., Shrestha, A. B., Ren, G., Rajbhandari, R., Saeed, S., Sanjay, J., Syed, M. A., Vellore, R., Xu, Y., You, Q., and Ren, Y.: Unravelling Climate Change in the Hindu Kush Himalaya: Rapid Warming in the Mountains and Increasing Extremes, Springer International Publishing, 57–97, ISBN 978-3-319-92288-1, https://doi.org/10.1007/978-3-319-92288-1_3, 2019. a
Lafon, T., Dadson, S., Buys, G., and Prudhomme, C.: Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods, Int. J. Climatol., 33, 1367–1381, https://doi.org/10.1002/joc.3518, 2013. a
Li, H., Haugen, J. E., and Xu, C.-Y.: Precipitation pattern in the Western Himalayas revealed by four datasets, Hydrol. Earth Syst. Sci., 22, 5097–5110, https://doi.org/10.5194/hess-22-5097-2018, 2018. a, b
Li, L., Gochis, D. J., Sobolowski, S., and Mesquita, M. D.: Evaluating the present annual water budget of a Himalayan headwater river basin using a high-resolution atmosphere-hydrology model, J. Geophys. Res., 122, 4786–4807, https://doi.org/10.1002/2016JD026279, 2017. a
Li, Z., Brissette, F., and Chen, J.: Finding the most appropriate precipitation probability distribution for stochastic weather generation and hydrological modelling in Nordic watersheds, Hydrol. Process., 27, 3718–3729, https://doi.org/10.1002/hyp.9499, 2013. a
Luo, X., Fan, X., Ji, X., and Li, Y.: Evaluation of corrected APHRODITE estimates for hydrological simulation in the Yarlung Tsangpo–Brahmaputra River Basin, Int. J. Climatol., 40, 4158–4170, https://doi.org/10.1002/JOC.6449, 2020. a
Lutz, A. F., Immerzeel, W. W., Kraaijenbrink, P. D., Shrestha, A. B., and Bierkens, M. F.: Climate change impacts on the upper indus hydrology: Sources, shifts and extremes, PLoS ONE, 11, e0165630, https://doi.org/10.1371/journal.pone.0165630, 2016. a
Maraun, D.: Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue, J. Climate, 26, 2137–2143, https://doi.org/10.1175/JCLI-D-12-00821.1, 2013. a
Maussion, F., Scherer, D., Finkelnburg, R., Richters, J., Yang, W., and Yao, T.: WRF simulation of a precipitation event over the Tibetan Plateau, China – an assessment using remote sensing and ground observations, Hydrol. Earth Syst. Sci., 15, 1795–1817, https://doi.org/10.5194/hess-15-1795-2011, 2011. a, b, c
Medina, S., Houze, R. A., Kumar, A., and Niyogi, D.: Summer monsoon convection in the Himalayan region: Terrain and land cover effects, Q. J. Roy. Meteor. Soc., 136, 593–616, https://doi.org/10.1002/qj.601, 2010. a
Momblanch, A., Papadimitriou, L., Jain, S. K., Kulkarni, A., Ojha, C. S., Adeloye, A. J., and Holman, I. P.: Untangling the water-food-energy-environment nexus for global change adaptation in a complex Himalayan water resource system, Sci. Total Environ., 655, 35–47, https://doi.org/10.1016/j.scitotenv.2018.11.045, 2019. a
Mukherji, A., Sinisalo, A., Nüsser, M., Garrard, R., and Eriksson, M.: Contributions of the cryosphere to mountain communities in the Hindu Kush Himalaya: A review, Reg. Environ. Change, 19, 1311–1326, https://doi.org/10.1007/s10113-019-01484-w, 2019. a
NASA Shuttle Radar Topography Mission (SRTM): Shuttle Radar Topography Mission (SRTM) Global, Earth Resources Observation and Science (EROS) Center [data set], https://doi.org/10.5066/F7PR7TFT, 2013. a
Nepal, S., Steiner, J. F., Allen, S., Azam, M. F., Bhuchar, S., Biemans, H., Dhakal, M., Khanal, S., Li, D., Lutz, A., Pradhananga, S., Ritzema, R., Stoffel, M., and Stuart-Smith, R.: Consequences of cryospheric change for water resources and hazards in the Hindu Kush Himalaya Consequences of cryospheric change for water resources and hazards in the Hindu Kush Himalaya, in: ICIMOD, edited by: Wester, P., Chaudhary, S., Chettri, N., Jackson, M., an Mah, A., 73–121, https://doi.org/10.53055/ICIMOD.1031, 2023. a
Nie, Y., Pritchard, H. D., Liu, Q., Hennig, T., Wang, W., Wang, X., Liu, S., Nepal, S., Samyn, D., Hewitt, K., and Chen, X.: Glacial change and hydrological implications in the Himalaya and Karakoram, Nature Reviews Earth and Environment, 2, 91–106, https://doi.org/10.1038/s43017-020-00124-w, 2021. a
Norris, J., Carvalho, L. M., Jones, C., Cannon, F., Bookhagen, B., Palazzi, E., and Tahir, A. A.: The spatiotemporal variability of precipitation over the Himalaya: evaluation of one-year WRF model simulation, Clim. Dynam., 49, 2179–2204, https://doi.org/10.1007/s00382-016-3414-y, 2017. a, b
Norris, J., Carvalho, L. M., Jones, C., and Cannon, F.: Deciphering the contrasting climatic trends between the central Himalaya and Karakoram with 36 years of WRF simulations, Clim. Dynam., 52, 159–180, https://doi.org/10.1007/s00382-018-4133-3, 2019. a, b, c
Norris, J., Carvalho, L. M., Jones, C., and Cannon, F.: Warming and drying over the central Himalaya caused by an amplification of local mountain circulation, npj Climate and Atmospheric Science, 3, 1, https://doi.org/10.1038/s41612-019-0105-5, 2020. a, b
Orr, A., Listowski, C., Couttet, M., Collier, E., Immerzeel, W. W., Deb, P., and Bannister, D.: Sensitivity of simulated summer monsoonal precipitation in Langtang Valley, Himalaya, to cloud microphysics schemes in WRF, J. Geophys. Res., 122, 6298–6318, https://doi.org/10.1002/2016JD025801, 2017. a, b, c, d
Orr, A., Ahmad, B., Alam, U., Appadurai, A. N., Bharucha, Z. P., Biemans, H., Bolch, T., Chaulagain, N. P., Dhaubanjar, S., Dimri, A. P., Dixon, H., Fowler, H. J., Gioli, G., Halvorson, S. J., Hussain, A., Jeelani, G., Kamal, S., Khalid, I. S., Liu, S., Lutz, A., Mehra, M. K., Miles, E., Momblanch, A., Muccione, V., Mukherji, A., Mustafa, D., Najmuddin, O., Nasimi, M. N., Nüsser, M., Pandey, V. P., Parveen, S., Pellicciotti, F., Pollino, C., Potter, E., Qazizada, M. R., Ray, S., Romshoo, S., Sarkar, S. K., Sawas, A., Sen, S., Shah, A., Shah, M. A. A., Shea, J. M., Sheikh, A. T., Shrestha, A. B., Tayal, S., Tigala, S., Virk, Z. T., Wester, P., and Wescoat, J. L.: Knowledge Priorities on Climate Change and Water in the Upper Indus Basin: A Horizon Scanning Exercise to Identify the Top 100 Research Questions in Social and Natural Sciences, Earth's Future, 10, e2021EF002619, https://doi.org/10.1029/2021EF002619, 2022. a, b
Palazzi, E., Hardenberg, J. V., and Provenzale, A.: Precipitation in the Hindu-Kush Karakoram Himalaya: Observations and future scenarios, J. Geophys. Res.-Atmos., 118, 85–100, https://doi.org/10.1029/2012JD018697, 2013. a, b, c
Pan, S. J. and Yang, Q.: A survey on transfer learning, IEEE T. Knowl. Data En., 22, 1345–1359, https://doi.org/10.1109/TKDE.2009.191, 2010. a
Peleg, N., Fatichi, S., Athanasios, P., Molnar, P., and Burlando, P.: An advanced stochastic weather generator for simulating 2-D high-resolution climate variables, J. Adv. Model. Earth Sy., 9, 1595–1627, https://doi.org/10.1002/2016MS000854, 2017. a
Potter, E. R., Orr, A., Willis, I. C., Bannister, D., and Salerno, F.: Dynamical Drivers of the Local Wind Regime in a Himalayan Valley, J. Geophys. Res.-Atmos., 123, 13186–13202, https://doi.org/10.1029/2018JD029427, 2018. a, b, c, d
Pritchard, H. D.: Global Data Gaps in Our Knowledge of the Terrestrial Cryosphere, Frontiers in Climate, 3, 1–7, https://doi.org/10.3389/fclim.2021.689823, 2021. a, b, c, d
Qazi, N. Q., Jain, S. K., Thayyen, R. J., Patil, P. R., and Singh, M. K.: Hydrology of the himalayas, Springer International Publishing, 419–450, ISBN 9783030296841, https://doi.org/10.1007/978-3-030-29684-1_21, 2019. a
Rasmussen, C. E. and Williams, C. K. I.: Gaussian Processes for Machine Learning, the MIT Press, ISBN 026218253X, 2006. a
Reichstein, M., Benson, V., Blunk, J., Camps-Valls, G., Creutzig, F., Fearnley, C. J., Han, B., Kornhuber, K., Rahaman, N., Schölkopf, B., Tárraga, J. M., Vinuesa, R., Dall, K., Denzler, J., Frank, D., Martini, G., Nganga, N., Maddix, D. C., and Weldemariam, K.: Early warning of complex climate risk with integrated artificial intelligence, Nat. Commun., 16, 2564, https://doi.org/10.1038/s41467-025-57640-w, 2025. a
Ren, Y. Y., Ren, G. Y., Sun, X. B., Shrestha, A. B., You, Q. L., Zhan, Y. J., Rajbhandari, R., Zhang, P. F., and Wen, K. M.: Observed changes in surface air temperature and precipitation in the Hindu Kush Himalayan region over the last 100-plus years, Advances in Climate Change Research, 8, 148–156, https://doi.org/10.1016/j.accre.2017.08.001, 2017. a, b
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning representations by back-propagating errors, Nature, 323, 533–536, https://doi.org/10.1038/323533a0, 1986. a
Sabin, T. P., Krishnan, R., Vellore, R., Priya, P., Borgaonkar, H. P., Singh, B. B., and Sagar, A.: Climate Change Over the Himalayas, Springer Singapore, 207–222, ISBN 9789811543272, https://doi.org/10.1007/978-981-15-4327-2_11, 2020. a
Sachindra, D. A., Ahmed, K., Rashid, M. M., Shahid, S., and Perera, B. J.: Statistical downscaling of precipitation using machine learning techniques, Atmos. Res., 212, 240–258, https://doi.org/10.1016/j.atmosres.2018.05.022, 2018. a
Saha, S., Moorthi, S., Pan, H. L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y. T., Chuang, H. Y., Juang, H. M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Delst, P. V., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Dool, H. V. D., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J. K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C. Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010. a
Salem, S., Siam, A., El-Dakhakhni, W., and Tait, M.: Probabilistic Resilience-Guided Infrastructure Risk Management, J. Manage. Eng., 36, 1–15, https://doi.org/10.1061/(asce)me.1943-5479.0000818, 2020. a
Salzmann, N., Huggel, C., Rohrer, M., and Stoffel, M.: Data and knowledge gaps in glacier, snow and related runoff research – A climate change adaptation perspective, J. Hydrol., 518, 225–234, https://doi.org/10.1016/j.jhydrol.2014.05.058, 2014. a
Samuel, J., Coulibaly, P., and Metcalfe, R. A.: Evaluation of future flow variability in ungauged basins: Validation of combined methods, Adv. Water Resour., 35, 121–140, https://doi.org/10.1016/j.advwatres.2011.09.015, 2012. a
Sanjay, J., Krishnan, R., Shrestha, A. B., Rajbhandari, R., and Ren, G. Y.: Downscaled climate change projections for the Hindu Kush Himalayan region using CORDEX South Asia regional climate models, Advances in Climate Change Research, 8, 185–198, https://doi.org/10.1016/j.accre.2017.08.003, 2017. a
Shea, J. M., Wagnon, P., Immerzeel, W. W., Biron, R., Brun, F., and Pellicciotti, F.: A comparative high-altitude meteorological analysis from three catchments in the Nepalese Himalaya, Int. J. Water Resour. D., 31, 174–200, https://doi.org/10.1080/07900627.2015.1020417, 2015. a
Shrestha, F., Steiner, J. F., Shrestha, R., Dhungel, Y., Joshi, S. P., Inglis, S., Ashraf, A., Wali, S., Walizada, K. M., and Zhang, T.: A comprehensive and version-controlled database of glacial lake outburst floods in High Mountain Asia, Earth Syst. Sci. Data, 15, 3941–3961, https://doi.org/10.5194/essd-15-3941-2023, 2023. a
Shrestha, M., Acharya, S. C., and Shrestha, P. K.: Bias correction of climate models for hydrological modelling - are simple methods still useful?, Meteorol. Appl., 24, 531–539, https://doi.org/10.1002/met.1655, 2017. a
Song, P. X.-K.: Vector Generalized Linear Models, Springer New York, 121–155, ISBN 978-0-387-71393-9, https://doi.org/10.1007/978-0-387-71393-9_6, 2007. a
Steiner, J. F., Gurung, T. R., Joshi, S. P., Koch, I., Saloranta, T., Shea, J., Shrestha, A. B., Stigter, E., and Immerzeel, W. W.: Multi-year observations of the high mountain water cycle in the Langtang catchment, Central Himalaya, Hydrol. Process., 35, e14189, https://doi.org/10.1002/hyp.14189, 2021. a
Sugiura, N.: Further Analysis of the data by Akaike’s information criterion and the finite corrections, Communications in Statistics – Theory and Methods, 7, 13–26, https://doi.org/10.1080/03610927808827599, 1978. a, b
Tazi, K., Orr, A., Hernandez-González, J., Hosking, S., and Turner, R. E.: Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5, Hydrol. Earth Syst. Sci., 28, 4903–4925, https://doi.org/10.5194/hess-28-4903-2024, 2024. a
Thayyen, R. J., Dimri, A. P., Kumar, P., and Agnihotri, G.: Study of cloudburst and flash floods around Leh, India, during August 4–6, 2010, Nat. Hazards, 65, 2175–2204, https://doi.org/10.1007/s11069-012-0464-2, 2013. a, b, c
Thornton, J. M., Pepin, N., Shahgedanova, M., and Adler, C.: Coverage of In Situ Climatological Observations in the World's Mountains, Frontiers in Climate, 4, 1–20, https://doi.org/10.3389/fclim.2022.814181, 2022. a
Turner, R. E., Diaconu, C.-D., Markou, S., Shysheya, A., Foong, A. Y. K., and Mlodozeniec, B.: Denoising Diffusion Probabilistic Models in Six Simple Steps, arXiv [preprint], https://doi.org/10.48550/arXiv.2402.04384, 2024. a
ul Hasson, S., Böhner, J., and Chishtie, F.: Low fidelity of CORDEX and their driving experiments indicates future climatic uncertainty over Himalayan watersheds of Indus basin, Clim. Dynam., 52, 777–798, https://doi.org/10.1007/s00382-018-4160-0, 2019. a, b
Vandal, T., Kodra, E., and Ganguly, A. R.: Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation, Theor. Appl. Climatol., 137, 557–570, https://doi.org/10.1007/s00704-018-2613-3, 2019. a
Vanschoren, J.: Meta-Learning: A Survey, arXiv [preprint], https://doi.org/10.48550/arXiv.1810.03548, 2018. a
Vaughan, A., Tebbutt, W., Hosking, J. S., and Turner, R. E.: Convolutional conditional neural processes for local climate downscaling, Geosci. Model Dev., 15, 251–268, https://doi.org/10.5194/gmd-15-251-2022, 2022. a, b
Wang, X., Tolksdorf, V., Otto, M., and Scherer, D.: WRF-based dynamical downscaling of ERA5 reanalysis data for High Mountain Asia: Towards a new version of the High Asia Refined analysis, Int. J. Climatol., 41, 743–762, https://doi.org/10.1002/joc.6686, 2021. a, b
Wester, P., Mishra, A., Mukherji, A., and Shrestha, A. B.: The Hindu Kush Himalaya Assessment – Mountains, Climate Change, Sustainability and People, Springer Nature Switzerland AG, Cham, ISBN 9783319922874, https://doi.org/10.1007/978-3-319-92288-1_8, 2019. a, b, c
Widmann, M., Blake, R., Sooraj, K., Orr, A., Sanjay, J., Karumuri, A., Mitra, A., Rajagopal, E., Loon, A. F. V., Hannah, D., Barrand, N., Singh, R., Mishra, V., Sudgen, F., and Arya, D.: Current opportunities and challenges in developing hydro-climatic services in the Himalayas: report of pump priming project November 2019, Tech. Rep., Centre for Ecology & Hydrology, Wallingford and Indian Institute of Tropical Meteorology, Pune, https://nora.nerc.ac.uk/id/eprint/528847/ (last access: 1 September 2024), 2019. a
Wit, A. J. W. D., Boogaard, H. L., and Diepen, C. A. V.: Spatial resolution of precipitation and radiation: The effect on regional crop yield forecasts, Agr. Forest Meteorol., 135, 156–168, https://doi.org/10.1016/j.agrformet.2005.11.012, 2005. a
Wong, G., Maraun, D., Vrac, M., Widmann, M., Eden, J. M., and Kent, T.: Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes, J. Climate, 27, 6940–6959, https://doi.org/10.1175/JCLI-D-13-00604.1, 2014. a, b
Wulf, H., Bookhagen, B., and Scherler, D.: Differentiating between rain, snow, and glacier contributions to river discharge in the western Himalaya using remote-sensing data and distributed hydrological modeling, Adv. Water Resour., 88, 152–169, https://doi.org/10.1016/j.advwatres.2015.12.004, 2016. a
Xu, J., Badola, R., Chettri, N., Chaudhary, R. P., Zomer, R., Pokhrel, B., Hussain, S. A., Pradhan, S., and Pradhan, R.: Sustaining Biodiversity and Ecosystem Services in the Hindu Kush Himalaya, Springer International Publishing, 127–165, ISBN 978-3-319-92288-1, https://doi.org/10.1007/978-3-319-92288-1_5, 2019. a
Yang, Y., Jin, M., Wen, H., Zhang, C., Liang, Y., Ma, L., Wang, Y., Liu, C., Yang, B., Xu, Z., Bian, J., Pan, S., and Wen, Q.: A Survey on Diffusion Models for Time Series and Spatio-Temporal Data, arXiv [preprint], https://doi.org/10.48550/arXiv.2404.18886, 2024. a
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh, A.: APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1, 2012. a
Zeiler, M. D. and Fergus, R.: Visualizing and Understanding Convolutional Networks, in: Computer Vision – ECCV 2014, edited by: David, F., Pajdla, T., Bernt, S., and Tinne, T., Springer International Publishing, 818–833, ISBN 978-3-319-10590-1, 2014. a
Ziarani, M. R., Bookhagen, B., Schmidt, T., Wickert, J., de la Torre, A., and Hierro, R.: Using convective available potential energy (CAPE) and dew-point temperature to characterize rainfall-extreme events in the South-Central Andes, Atmosphere, 10, 379, https://doi.org/10.3390/atmos10070379, 2019. a
Short summary
We introduce a novel method for improving daily precipitation maps in mountain regions and pilot it across three basins in the Hindu Kush Himalaya (HKH). The approach leverages climate model and weather station data, along with statistical or machine learning techniques. Our results show that this approach outperforms traditional methods, especially in remote ungauged areas, suggesting that it could be used to improve precipitation maps across much of the HKH, as well as other mountain regions.
We introduce a novel method for improving daily precipitation maps in mountain regions and pilot...