The research, led by Vineet Kumar Sharma of the Department of Civil Engineering at Malaviya National Institute of Technology Jaipur, together with Archana Sarkar of the National Institute of Hydrology in Roorkee and Rohit Goyal of Malaviya National Institute of Technology, is among the first detailed basin-scale assessments of future short-duration precipitation extremes for the Banas Basin based on bias-corrected projections from the latest generation of global climate models. The team analyzed outputs from 13 General Circulation Models participating in the Coupled Model Intercomparison Project Phase 6, known as CMIP6, which underpins much of the current international assessment of climate change.

To measure extremes, the researchers focused on three widely used precipitation indices: Rx1day, which captures the maximum rainfall falling in a single day; Rx3day, the maximum accumulated over three consecutive days; and Rx5day, the maximum over five consecutive days. These indices matter because they correspond directly to the kinds of rainfall bursts that overwhelm drainage systems, saturate soils, and trigger flash floods and runoff. The team established a historical baseline using observations from 1985 to 2019, then examined three future windows: the near-century from 2020 to 2040, the mid-century from 2041 to 2070, and the far-century from 2071 to 2100.

The projections were run under four Shared Socioeconomic Pathways, the standard scenario framework used in CMIP6: SSP1-2.6, a low-emission pathway consistent with strong climate mitigation; SSP2-4.5, an intermediate scenario; and SSP3-7.0 and SSP5-8.5, high-emission futures in which greenhouse gas concentrations continue to rise substantially through the century. Comparing outcomes across these scenarios allows the study to separate the signal of climate change from natural variability and to quantify how much the trajectory of emissions shapes the basin’s future flood hazard.

During the historical period, the basin’s extreme rainfall stayed within relatively narrow bounds. The maximum one-day precipitation, Rx1day, generally remained below 200 millimeters, while the three-day and five-day maxima stayed below roughly 370 and 420 millimeters respectively. Those figures define the hydrological envelope within which the region’s infrastructure, reservoirs, and agricultural systems have evolved. The projections show that envelope beginning to stretch almost immediately, and stretching much further as emissions climb.

In the near-century period from 2020 to 2040, the low-emission SSP1-2.6 scenario projects only modest increases in extreme rainfall. But under SSP3-7.0 and SSP5-8.5, the study finds a widespread strengthening of short-duration precipitation extremes across the basin. Notably, these near-term projections already exceed the historical maxima in places, signaling what the authors describe as an early emergence of extremes: rainfall events beyond anything recorded in the observational record could arrive within the working lifetime of today’s engineers and water managers, even before mid-century.

The mid-century picture sharpens the contrast between emission pathways. By 2041 to 2070, SSP1-2.6 still shows relatively limited change, but under SSP3-7.0 and SSP5-8.5 the maximum one-day rainfall is projected to climb to approximately 260 to 380 millimeters, and the three-day maximum to between 470 and 670 millimeters. Those three-day totals would surpass the historical five-day ceiling of around 420 millimeters, meaning that a three-day storm late this century could deliver more rain than the most extreme five-day events of the past several decades.

The most pronounced changes arrive in the far-century period. Under SSP5-8.5, during 2071 to 2100, the five-day maximum precipitation exceeds 720 to 820 millimeters across several parts of the basin. That figure, nearly double the historical five-day maximum, points to a considerable rise in prolonged heavy-rainfall events, the kind of sustained deluges that produce the largest runoff volumes and pose the greatest challenge to dams, embankments, and downstream communities. The study emphasizes that both the intensity and the spatial extent of extreme precipitation are projected to increase as greenhouse gas emissions rise, meaning larger areas of the basin would be exposed to severe events, not just isolated hotspots.

The paradox at the heart of the findings is that these intensifying extremes are occurring in a landscape defined by water scarcity. The Banas Basin lies within the semi-arid, desert-influenced state of Rajasthan, a region long associated with drought, erratic monsoons, and chronic water stress. Previous research in the basin has documented trends in dry and wet spells, agricultural drought, and climate impacts on crop yields, and the basin’s semi-arid character has traditionally shaped planning around managing too little water rather than too much. The new projections suggest that both problems will coexist: longer and deeper dry periods punctuated by rainfall events of unprecedented ferocity. This combination is particularly hazardous, because degraded, sparsely vegetated semi-arid soils absorb water poorly, so intense rain translates rapidly into flash floods and erosion rather than groundwater recharge.

The authors frame their results as scientific evidence to support flood-risk assessment, reservoir operation, watershed management, and climate adaptation planning in the basin. The practical implications are concrete: design standards for drainage and flood-control infrastructure based on historical rainfall statistics may systematically underestimate future extremes; reservoir operating rules calibrated to past inflow patterns may need revision; and watershed management programs aimed at soil and moisture conservation will have to account for runoff regimes that differ sharply from the historical record. Because the signal strengthens with emissions, the study also carries a broader message: the difference between a low-emission future with manageable changes and a high-emission future with transformative flood risk is still, in large part, a policy choice. For the semi-arid Banas Basin, a region on the front line of both drought and, increasingly, deluge, that choice will help determine whether its rivers are managed as scarce resources or defended against as sudden threats.

Subject of Research: Projected changes in extreme precipitation events in the semi-arid Banas River Basin of Rajasthan, India, using bias-corrected CMIP6 climate model projections

Article Title: Future change in extreme precipitation events in semi-arid Banas River Basin

Article References: Sharma, V. K., Sarkar, A., & Goyal, R. (2026). Future change in extreme precipitation events in semi-arid Banas River Basin. Theoretical and Applied Climatology, 157(10), Article 638. https://doi.org/10.1007/s00704-026-06552-w

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06552-w

Keywords: extreme precipitation, Banas River Basin, CMIP6, climate change, Rajasthan, semi-arid region, flood risk, Shared Socioeconomic Pathways, Rx1day, flash floods, climate adaptation, monsoon

The research team, led by Ruixin Duan of the National Institute of Natural Hazards under China’s Ministry of Emergency Management, together with colleagues from Beijing Normal University, the University of Regina, Beijing University of Technology, and the University of Waterloo, constructed what they describe as a dual-dimension attribution framework. Rather than relying on a single statistical lens, the framework integrates two complementary approaches: multi-threshold probability ratios, which compare the likelihood of extreme events in a world with human influences against a hypothetical world without them, and nonlinear scaling analysis, which examines how the strength of that human influence changes as events grow progressively rarer and more severe.

To anchor their analysis in reality, the researchers used the CN05.1 observational dataset, a high-resolution gridded product built from weather station records across mainland China. Against this observational baseline, they deployed simulations from the Coupled Model Intercomparison Project Phase 6, known as CMIP6, the international ensemble of state-of-the-art climate models that underpins much of modern climate science, including the assessments of the Intergovernmental Panel on Climate Change. By comparing simulations that include all human and natural forcings with simulations driven only by natural factors such as solar variability and volcanic eruptions, the team could isolate the signature of human influence on rainfall extremes.

The observational record itself tells a compelling story. Between 1961 and 2020, two widely used extreme precipitation indices — Rx1day, which captures the maximum rainfall falling in a single day, and Rx5day, which measures the heaviest five-day accumulation — exhibited widespread upward trends across China. The study found that 61.4 percent of all grid cells showed positive trends in Rx1day, while 56.9 percent showed positive trends in Rx5day. In other words, across most of the country’s territory, the wettest days and wettest weeks of each year have been getting wetter, a pattern consistent with the fundamental physics of a warming atmosphere.

That physics is worth spelling out. For every degree Celsius of warming, the atmosphere can hold roughly seven percent more water vapor, following the Clausius–Clapeyron relationship. A moister atmosphere provides more fuel for storms, and observations and models alike show that extreme precipitation tends to intensify faster than average rainfall, because the extra moisture is disproportionately funneled into the heaviest events. In some circumstances, particularly for short-duration convective storms, intensification can even exceed the seven-percent-per-degree benchmark — a phenomenon known as super-Clausius–Clapeyron scaling that recent research has linked to shifts from stratiform to convective rain types within storm systems.

The heart of the new study, however, lies in its probability ratio calculations — the currency of modern event attribution science. The probability ratio expresses how many times more likely an event of a given severity has become under human influence compared with a counterfactual climate shaped only by natural forces. For 100-year return period events — downpours so severe that, in a stable climate, they would be expected only once per century — the results are sobering. Under the ALL-forcing scenario, which includes both anthropogenic and natural drivers, the probability of 100-year Rx1day events was approximately 1.84 times that under the NAT-forcing scenario, which includes natural drivers alone. For five-day extremes of the same rarity, the probability ratio was approximately 1.38. A once-in-a-century deluge has effectively become a once-in-54-years event in the case of the one-day extreme, according to these multi-model estimates.

But the truly novel finding emerges when the researchers examined how these probability ratios change with return period. Using a log–log scaling analysis that plots probability ratios against return periods on logarithmic axes, the team uncovered a consistent tendency: probability ratios tend to rise as return periods lengthen. In practical terms, human influence is not merely shifting the entire distribution of rainfall upward — it appears to be disproportionately amplifying the extreme tail, where the most catastrophic and least frequent events reside. Higher probability ratio values were generally associated with longer return periods, suggesting that the rarest, most destructive storms are precisely where anthropogenic forcing leaves its deepest statistical mark.

This nonlinear relationship between forcing and event rarity carries profound implications for risk management. Infrastructure in China — and indeed worldwide — is designed around return periods: dams, urban drainage networks, and flood defenses are typically engineered to withstand 50-year, 100-year, or occasionally 1,000-year events. If those events are becoming substantially more probable, the engineering assumptions embedded in decades-old design standards are quietly eroding. The 2021 record-breaking rainfall around Henan Province, which the study’s authors have examined in earlier work, and the 2023 Beijing–Tianjin–Hebei extreme rainfall event both serve as vivid reminders of what happens when precipitation exceeds the thresholds that infrastructure was built to handle.

The authors are careful to emphasize that the magnitude of the anthropogenic response varies across regions and remains subject to uncertainty — a candid acknowledgment that reflects the genuine challenges of regional attribution science. China spans tropical, subtropical, temperate, and alpine climate zones, and the response of precipitation extremes to forcing differs markedly among them. Confounding factors, including anthropogenic aerosols that can locally suppress rainfall even as greenhouse gases enhance it, internal climate variability, and the coarse resolution of global models in resolving complex topography such as the Tibetan Plateau, all contribute to the uncertainty envelope. Previous studies have documented both detectable human influence on precipitation extremes across China and locally divergent responses, underscoring that attribution is as much about quantifying confidence as about delivering headline numbers.

Even so, the study’s central message lands with force: human activities appear to be enhancing the likelihood of extreme precipitation events in China, particularly the rarer ones, and assessments of climate risk must account for changes in this tail risk rather than focusing solely on shifts in average conditions. As global temperatures continue to climb, the framework developed here — combining multi-threshold probability ratios with nonlinear scaling — offers a template that can be applied beyond China’s borders. For adaptation planners, the implications are clear: the storms once dismissed as statistical outliers are becoming statistical neighbors, and planning for the climate of the coming decades means planning for a distribution whose extremes are moving faster than its center.

Subject of Research: Anthropogenic influence on extreme precipitation events and their return periods in China

Article Title: Anthropogenic forcing enhances extreme precipitation with increasing return period in China

Article References: Duan, R., Zhong, L., Huang, G., Wang, F., Zhang, S., & Tian, C. (2026). Anthropogenic forcing enhances extreme precipitation with increasing return period in China. Climate Dynamics, 64(10), Article 422. https://doi.org/10.1007/s00382-026-08334-6

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08334-6

Keywords: extreme precipitation, anthropogenic climate change, CMIP6, event attribution, China, return period, Rx1day, Rx5day, probability ratio, nonlinear scaling, tail risk, climate adaptation