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Water
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Analysis of Extreme Precipitation Under Climate Change, 2nd Edition
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Analysis of Extreme Precipitation Under Climate Change, 2nd Edition

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 975

Special Issue Editor

Dr. Xiaosheng Qin

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: hydrological modeling; extreme weather analysis; climate change impact assessment and adaptative planning; water resources and environmental systems planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change is anticipated to have a profound impact on precipitation patterns worldwide. Regions already susceptible to heavy rainfall may face more frequent and severe flooding, while those accustomed to snowfall might encounter intensified and prolonged snowstorms. Moreover, alterations in atmospheric circulation patterns due to climate change could disrupt the distribution and timing of precipitation, potentially exacerbating drought conditions in certain areas and leading to shifts in seasonal precipitation patterns. These changes may carry extensive implications for water resource management, infrastructure resilience, ecosystem dynamics, and community vulnerability. Hence, this Special Issue endeavors to explore the intricate relationship between extreme precipitation events and the broader context of climate change. Topics of interest include extreme precipitation analysis, joint-probabilistic risks of compound events, climate model downscaling, bias correction of precipitation data, hydrological impacts of climate change, and other relevant topics. By delving into these themes, this Special Issue aims to advance our comprehension of extreme precipitation events within the framework of climate change, fostering interdisciplinary dialog among researchers, policymakers, and practitioners to address these pressing concerns.

Dr. Xiaosheng Qin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • extreme precipitation
  • climate change
  • downscaling
  • compound events
  • hydrological impact

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Related Special Issue

Published Papers (1 paper)

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Research

15 pages, 3549 KB  
Article
Application and Comparison of Two Transformer-Based Deep Learning Models in Short-Term Precipitation Nowcasting
by Chuhan Lu and Qilong Pan
Water 2026, 18(6), 757; https://doi.org/10.3390/w18060757 - 23 Mar 2026
Viewed by 685
Abstract
Against the background of intensifying global climate change, extreme precipitation events have become increasingly frequent. Improving the accuracy of short-term precipitation nowcasting is therefore essential for disaster prevention and mitigation. Traditional numerical weather prediction (NWP) approaches are constrained by computational latency and errors [...] Read more.
Against the background of intensifying global climate change, extreme precipitation events have become increasingly frequent. Improving the accuracy of short-term precipitation nowcasting is therefore essential for disaster prevention and mitigation. Traditional numerical weather prediction (NWP) approaches are constrained by computational latency and errors arising from physical parameterizations, making it difficult to satisfy real-time forecasting requirements at high spatiotemporal resolution. Using the SEVIR dataset, this study conducts a systematic comparison of two Transformer-based deep learning models—Earthformer and LLMDiff—for short-term extreme precipitation nowcasting. Model performance is evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and Success Ratio (SUCR). Results indicate that, for 0–30 min lead times, Earthformer more efficiently captures both local and long-range spatiotemporal dependencies via its Cuboid Attention mechanism and shows a slight advantage for low-intensity precipitation. As the lead time extends to 60 min, LLMDiff demonstrates stronger longer-horizon skill due to its diffusion-based probabilistic modeling and a frozen large language model (LLM) module, which enhance the representation of uncertainty and longer-term evolution of precipitation systems. However, LLMDiff tends to produce a higher false-alarm rate. Overall, Earthformer is better suited for rapid early warning of light precipitation, whereas LLMDiff is more appropriate for high-accuracy nowcasting of heavy precipitation, offering useful insights for intelligent forecasting of extreme weather. Full article
(This article belongs to the Special Issue Analysis of Extreme Precipitation Under Climate Change, 2nd Edition)
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