Future Precipitation Changes Research Articles

Modeling of historical and future changes in temperature and precipitation in the Panj River Basin in Central Asia under the CMIP5 RCP and CMIP6 SSP scenarios

This study examines the complexities of climate modeling, specifically in the Panj River Basin (PRB) in Central Asia, to evaluate the transition from CMIP5 to CMIP6 models. The research aimed to identify differences in historical simulations and future predictions of rainfall and temperature, examining the accuracy of eight General Circulation Models (GCMs) used in both CMIP5 (RCP4.5 and 8.5) and CMIP6 (SSP2–4.5 and 5–8.5). The evaluation metrics demonstrated that the GCMs have a high level of accuracy in reproducing maximum temperature (Tmax) with a correlation coefficient of 0.96. The models also performed well in replicating minimum temperature (Tmin) with a correlation coefficient of 0.94. This suggests that the models have improved modeling capabilities in both CMIPs. The performance of Max Plank Institute (MPI) across all variables in CMIP6 models was exceptional. Within the CMIP5 domain, Geophysical Fluid Dynamics (GFDL) demonstrated outstanding skill in reproducing maximum temperature (Tmax) and precipitation (KGE 0.58 and 0.34, respectively), while (Institute for Numerical Mathematics) INMCM excelled in replicating minimum temperature (Tmin) (KGE 0.28). The uncertainty analysis revealed a significant improvement in the CMIP6 precipitation bias bands, resulting in a more precise depiction of diverse climate zones compared to CMIP5. Both CMIPs consistently tended to underestimate Tmax in the Csa zone and overestimate it in the Bwk zone throughout all months. Nevertheless, the CMIP6 models demonstrated a significant decrease in uncertainty, especially in ensemble simulations, suggesting improvements in forecasting PRB climate dynamics. The projections revealed a complex story, as the CMIP6 models predict a relatively small increase in temperature and a simultaneous drop in precipitation. This indicates a trend towards more uniform temperature patterns across different areas. Nevertheless, the precipitation forecasts exhibited increased variability, highlighting the intricate interaction of climate dynamics in the PRB area under the impact of global warming scenarios. Hydrological components in global climate models can be further improved and developed with the theoretical reference provided by this study.

Reduced aerosols and intensified summertime rainfall in India during the pandemic suggest potentially more amplified precipitation in the future

Abstract Aerosol pollution is anticipated to decrease in the future, yet the associated effects of reduced aerosol loading on precipitation remain insufficiently explored. Widespread reductions in anthropogenic emission during COVID-19 lockdowns offer a unique opportunity to understand precipitation responses to changes in anthropogenic aerosols. Based on observations and regional and global climate-chemistry coupled model simulations, we attribute unprecedented precipitation in India during the 2021 lockdown to decreased aerosol levels due to emission reductions. Reduced aerosol loading leads to a northward shift of the subtropical westerly jet, which induces a westward movement of the subtropical southern branch trough and negative sea-level pressure anomalies over the eastern Arabian Sea. This shift facilitates greater water vapor transport from surrounding oceans to land, increasing precipitation in India by approximately 24.2% in May according to WRF-Chem simulations and by 28.5% over the entire lockdown period according to CESM2 simulations. Future projections under the lower aerosol emission scenario indicate an additional enhancement in monsoon precipitation in India. Our findings highlight the complex interplay between aerosol emissions and hydrometeorological dynamics, with implications for understanding future precipitation changes and providing theoretical reference for water resource management.

Increasing certainty in projected local extreme precipitation change

The latest climate models project widely varying magnitudes of future extreme precipitation changes, thus impeding effective adaptation planning. Many observational constraints have been proposed to reduce the uncertainty of these projections at global to sub-continental scales, but adaptation generally requires detailed, local scale information. Here, we present a temperature-based adaptative emergent constraint strategy combined with data aggregation that reduces the error variance of projected end-of-century changes in annual extremes of daily precipitation under a high emissions scenario by >20% across most areas of the world. These improved projections could benefit nearly 90% of the world’s population by permitting better impact assessment and adaptation planning at local levels. Our physically motivated strategy, which considers the thermodynamic and dynamic components of projected extreme precipitation change, exploits the link between global warming and the thermodynamic component of extreme precipitation. Rigorous cross-validation provides strong evidence of its reliability in constraining local extreme precipitation projections.

Future large-scale atmospheric circulation changes and Greenland precipitation

In this work, we examine connections between patterns of future Greenland precipitation and large-scale atmospheric circulation changes over the Northern Hemisphere. In the last three decades of the 21st century, CMIP5 and CMIP6 ensemble mean precipitation significantly decreases over the northern part of the North Atlantic Ocean with respect to 1951–1980. This drying signal extends from the ocean to the southeastern margin of Greenland. The 500 hPa geopotential height change shows a clear pattern including a widespread increase across the Arctic with a negative anomaly centered over Iceland and surrounding regions. To identify the mechanisms linking atmospheric circulation variability with Greenland precipitation, we perform a singular value decomposition (SVD) and center of action (COA) analysis. We find that a northeastward shift of the Icelandic Low (IL) under the SSP5‐8.5 warming scenario leads to the drying signal found in southeast Greenland. This implies that the IL location will have a strong influence on precipitation changes over southeast Greenland in the future, impacting projections of Greenland ice sheet surface mass balance.

On the Extrapolation of Generative Adversarial Networks for Downscaling Precipitation Extremes in Warmer Climates

AbstractWhile deep‐learning downscaling algorithms can generate fine‐scale climate projections cost‐effectively, it is unclear how effectively they extrapolate to unobserved climates. We assess the extrapolation capabilities of a deterministic Convolutional Neural Network baseline and a Generative Adversarial Network (GAN) built with this baseline, trained to predict daily precipitation simulated by a Regional Climate Model (RCM) over New Zealand. Both approaches emulate future changes in annual mean precipitation well, when trained on historical data, though training on a future climate improves performance. For extreme precipitation (99.5th percentile), RCM simulations predict a robust end‐of‐century increase with future warming (∼5.8%/C on average from five simulations). When trained on a future climate, GANs capture 97% of the warming‐driven increase in extreme precipitation compared to 65% in a deterministic baseline. Even GANs trained historically capture 77% of this increase. Overall, GANs offer better generalization for downscaling extremes, which is important in applications relying on historical data.

Precipitation Prediction in Suqian City, Jiangsu Province of China Using LSTM Based on Grey Wolf Optimizer Algorithm

To enhance urban planning and disaster management in the context of global climate change, we focused on precipitation forecasting in Suqian City, Jiangsu Province, and established a Long Short-Term Memory (LSTM) model optimized by the Grey Wolf Optimizer (GWO) algorithm. Firstly, we collected five years of precipitation data to ensure a solid foundation for model training. Secondly, we integrated the GWO algorithm to optimize the LSTM model parameters, thereby improving prediction accuracy. Through data preprocessing, model establishment, and validation, we successfully constructed a precipitation forecasting model tailored for both rainy and non-rainy seasons in Suqian. Lastly, utilizing this model, we predicted the precipitation volume for the upcoming 12 months in Suqian and proposed targeted countermeasures and suggestions based on the forecast results to guide urban planning and disaster management efforts, ensuring that Suqian can effectively address the challenges posed by future precipitation changes. Additionally, by combining actual conditions with predictive data, we refined and optimized Suqian's precipitation management strategies.

Projected future changes in extreme precipitation over China under stratospheric aerosol intervention in the UKESM1 climate model

Abstract. Extreme precipitation events are linked to severe economic losses and casualties in China every year; hence, exploring the potential mitigation strategies to minimize these events and their changes in frequency and intensity under global warming is of importance, particularly for the populous subregions. In addition to global warming scenarios, this study examines the effects of the potential deployment of stratospheric aerosol injection (SAI) on hydrological extremes in China based on the SAI simulations (G6sulfur) of the Geoengineering Model Intercomparison Project (GeoMIP) by the UK Earth System Model (UKESM1) simulations. G6sulfur is compared with simulations of the future climate under two different emission scenarios (SSP5-8.5 and SSP2-4.5) and a reduction in the solar constant (G6solar) to understand the effect of SAI on extreme precipitation patterns. The results show that under global warming scenarios, precipitation and extreme wet climate events during 2071–2100 are projected to increase relative to the control period (1981–2010) across all the subregions in China. Extreme drought events show a projected increase in southern China. The G6sulfur and G6solar experiments show statistically similar results to those under SSP2-4.5 in extreme precipitation intensities of China in UKESM1. These results are encouraging. The efficacy of SAI in decreasing extreme precipitation events and consecutive wet days is more pronounced than that of G6solar when compared to SSP2-4.5. While both G6sulfur and G6solar show drying at high-latitude regions, which is consistent with our understanding of the spin-down of the hydrological cycle under SRM. Given the limitations of the current model and the small ensemble size, and considering that the hydrological effects are less beneficial than those indicated for temperature, it is recommended that further, more comprehensive research be performed, including using multiple models, to better understand these impacts.

On the present and future changes in Indian summer monsoon precipitation characteristics under different SSP scenarios from CMIP6 models

Monsoons are a vital part of the agriculture and economy of India which most of its population rely on for their livelihoods. It still is not clear how climate change will impact precipitation events over India due to the complexity of accurately modelling precipitation. Using twelve Coupled Model Intercomparison Project Six (CMIP6) models, we compared their performance to observed data taken from CRU as well as looking at the future changes in precipitation until the end of the twenty first century for the six precipitation homogenous regions over India. The individual models showed varying degrees of wet and dry biases and the ensemble mean of these models showed relatively lesser bias and improved spatial correlation. Out of 12 models, NorESM and MIROC6 models outperform other models in terms of capturing the spatial variability of precipitation over the Indian region. It is also found that due to lesser moisture transport from the adjoining seas represented through vertically integrated moisture transport (VIMT) analysis, there is consistent dry bias across the models. Further a comprehensive analysis of model performance across six homogeneous precipitation regions indicates that NorESM demonstrates better performance in the CNE and HR regions, EC-Earth excels in the PR, WC, and NE regions, while CMCC shows better performance specifically in the NW region compared to other models. Shared Socioeconomic Pathways (SSPs) were used for future projections and a slight increase in June, July, August, and September (JJAS) precipitation until the end of the century with SSP5-8.5 showing the largest increase. We found an increase in precipitation of 0.49, 0.74 and 1.4 mm/day under SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the far future. The northeast region was shown to receive the largest increase in precipitation (2.9 mm/day) compared to other precipitation homogenous regions and northwest will experience largest shift in precipitation. Interestingly, the number of wet days is expected to increase in the northwest region implying more VIMT towards the region. Our results indicate that monsoon precipitation extremes across all the homogenous regions will increase into the future with a higher severity under fossil-fuelled development, although the models still show large biases lowering confidence in our results.

Identifying Factors Mediating the Carnivore Richness–Productivity Relationship Across a Precipitation‐Driven Ecotone

ABSTRACTAimPrimary productivity is an important mechanism influencing the species richness of animal taxa such as mammals. However, the factors that mediate the relationship between carnivore species richness, for example, and productivity are not well understood. We examined whether the relationship between carnivore species richness and primary productivity followed a hump‐shaped relationship and explored potential pathways by which productivity influences carnivore communities.LocationWestern Great Basin, USA.MethodsWe deployed a camera‐trap grid across a strong precipitation—and the corresponding productivity—gradient and estimated carnivore species richness using a novel application of a multisession community occupancy model. To quantify the relationship between carnivore species richness and productivity, we included the linear and quadratic effects of average annual precipitation as covariates on carnivore species richness. We fit additional community models linking carnivore species richness to vegetation and land cover variables to identify factors that mediate the relationship between carnivore richness and productivity.ResultsThe linear effect of precipitation on carnivore richness was positive and the quadratic effect was negative, supporting the hump‐shaped relationship where richness declines at the highest levels of productivity. Similarly, carnivore species richness followed a quadratic relationship with vegetation height along the productivity gradient. The relationship between carnivore species richness and proportion tree cover was positive, with limited evidence that open vegetation types in the surrounding landscape increased site‐level richness.Main ConclusionsIn addition to supporting the notion that primary productivity has a strong and nonlinear influence on local‐scale carnivore species richness, our results suggest that vegetation structure may be an important mediator of this relationship. Although future changes in precipitation and productivity due to climate change are uncertain, our results indicate that targeting areas of intermediate productivity and vegetation height, such as woodlands, may be key for conserving species‐rich carnivore communities along productivity gradients.

Breaking Rossby waves drive extreme precipitation in the world’s arid regions

More than a third of the world’s population lives in drylands and is disproportionately at risk from hydrometeorological hazards such as drought and flooding. While weather systems governing precipitation formation in humid regions have been widely explored, our understanding of the atmospheric processes generating precipitation in arid regions remains fragmented at best. Here we show, using a variety of precipitation datasets, that Rossby wave breaking is a key atmospheric driver of precipitation in arid regions worldwide. Rossby wave breaking contributes up to 90% of daily precipitation extremes and up to 80% of total precipitation amounts in arid regions equatorward and downstream of the midlatitude storm tracks. The relevance of Rossby wave breaking for precipitation increases with increasing land aridity. Contributions of wave breaking to precipitation dominate in the poleward and westward portions of arid subtropical regions during the cool season. Our findings imply that Rossby wave breaking plays a crucial role in projections and uncertainties of future precipitation changes in societally vulnerable regions that are exposed to both freshwater shortages and flood hazards.

Global and regional hydrological impacts of global forest expansion

Abstract. Large-scale reforestation, afforestation, and forest restoration schemes have gained global support as climate change mitigation strategies due to their significant carbon dioxide removal (CDR) potential. However, there has been limited research into the unintended consequences of forestation from a biophysical perspective. In the Community Earth System Model version 2 (CESM2), we apply a global forestation scenario, within a Paris Agreement-compatible warming scenario, to investigate the land surface and hydroclimate response. Compared to a control scenario where land use is fixed to present-day levels, the forestation scenario is up to 2 °C cooler at low latitudes by 2100, driven by a 10 % increase in evaporative cooling in forested areas. However, afforested areas where grassland or shrubland are replaced lead to a doubling of plant water demand in some tropical regions, causing significant decreases in soil moisture (∼ 5 % globally, 5 %–10 % regionally) and water availability (∼ 10 % globally, 10 %–15 % regionally) in regions with increased forest cover. While there are some increases in low cloud and seasonal precipitation over the expanded tropical forests, with enhanced negative cloud radiative forcing, the impacts on large-scale precipitation and atmospheric circulation are limited. This contrasts with the precipitation response to simulated large-scale deforestation found in previous studies. The forestation scenario demonstrates local cooling benefits without major disruption to global hydrodynamics beyond those already projected to result from climate change, in addition to the cooling associated with CDR. However, the water demands of extensive forestation, especially afforestation, have implications for its viability, given the uncertainty in future precipitation changes.

Relative contribution of dynamic and thermodynamic components on Southeast Asia future precipitation changes from different multi-GCM ensemble members

To address the gap in understanding precipitation changes in Southeast Asia and to enhance the reliability of climate projections for the region through moisture budget analysis, this study examines the differences among six multi-model ensembles of CMIP6 simulated precipitation in term of moisture budget analysis. It investigates the relative contributions of thermodynamic and dynamic components to seasonal precipitation changes over Southeast Asia under the highest emission scenario, SSP5-8.5. The comparison between ensembles indicates that Good performance model ensembles slightly outperform the combination of all resolution and all category ensembles in reducing the biases. There is no strong evidence showing that good category ensembles outperform the combination of all model ensemble groups in simulating the spatial pattern of historical seasonal precipitation. From the perspective of moisture budget, regions receiving seasonal high rainfall intensity are mainly influenced by the moisture convergence during the monsoon seasons: northeast monsoon (December‒January‒February) and southwest monsoon (June–July–August). By the late 21st century (2081–2100), all model ensemble projections show an increase in December‒January‒February precipitation over the northern Southeast Asia and decreased June‒July‒August rainfall in the southern regions. The moisture budget analysis explained that the seasonal mean rainfall change in Southeast Asia is largely influenced by evaporation and followed by moisture flux convergence. The changes in moisture flux convergence are contributed by both the dynamic and thermodynamic components. Greater inter-model uncertainty was found in the precipitation dynamic component compared to the thermodynamic component suggesting the existence of large discrepancy between the various approaches used by GCMs in describing atmospheric dynamics. The study highlights that the Good model ensemble with middle to low resolution is able to narrow the inter-model uncertainties in terms of the moisture budget analysis compared to the combination of all Good model ensembles.

Linking Future Precipitation Changes to Weather Features in CESM2‐LE

AbstractWeather features, such as extratropical cyclones, atmospheric rivers (ARs), and fronts, contribute to substantial amounts of precipitation globally and are associated with different precipitation characteristics. However, future changes in these characteristics, as well as their representation in climate models, remain uncertain. We attribute 6‐hourly accumulated precipitation to cyclones, moisture transport axes (AR‐like features), fronts, and cold air outbreaks, and the combinations thereof in 10 ensemble members of the CESM2‐LE between 1960 and 2100 under the SSP3‐7.0 scenario. We find that, despite some biases in both precipitation and weather features, CESM2‐LE adeptly represents the precipitation characteristics associated with the different combinations of weather features. The combinations of weather features that contribute most to precipitation in the present climate also contribute the most to future changes, both due to changes in intensity as well as frequency. While the increase in precipitation intensity dominates the overall response for total precipitation in the storm track regions, the precipitation intensity for the individual weather features does not necessarily change significantly. Instead, approximately half of the increase in precipitation intensity in the storm track regions can be attributed to a higher occurrence of the more intensely precipitating combinations of weather features, such as the co‐occurrence of extratropical cyclones, fronts, and moisture transport axes.

Precipitation changes alter plant dominant species and functional groups by changing soil salinity in a coastal salt marsh

Specific mechanisms of precipitation change due to global climate variability on plant communities in coastal salt marsh ecosystems remain unknown. Hence, a field manipulative precipitation experiment was established in 2014 and 5 years of field surveys of vegetation from 2017 to 2021 to explore the effects of precipitation changes on plant community composition. The results showed that changes in plant community composition were driven by dominant species, and that the dominance of key species changed significantly with precipitation gradient and time, and that these changes ultimately altered plant community traits (i.e., community density, height, and species richness). Community height increased but community density decreased with more precipitation averaged five years. Furthermore, changes in precipitation altered dominant species composition and functional groups mainly by influencing soil salinity. Salinity stress caused by decreased precipitation shifted species composition from a dominance of taller perennials and grasses to dwarf annuals and forbs, while the species richness decreased. Conversely, soil desalination caused by increased precipitation increased species richness, especially increasing in the dominance of grasses and perennials. Specifically, Apocynaceae became dominance from rare while Amaranthaceae decreased in response to increased precipitation, but Poaceae was always in a position of dominance. Meanwhile, the dominance of grasses and perennials has the cumulative effect of years and their proportion increased under the increased 60% of ambient precipitation throughout the years. However, the annual forb Suaeda glauca was gradually losing its dominance or even becoming extinct over years. Our study highlights that the differences in plant salinity tolerance are key to the effects of precipitation changes on plant communities in coastal salt marsh. These findings aim to provide a theoretical basis for predicting vegetation dynamics and developing ecological management strategies to adapt to future precipitation changes.

Internally Driven Variability of the Angola Low is the Main Source of Uncertainty for the Future Changes in Southern African Precipitation

AbstractVariations in southern African precipitation have a major impact on local communities, increasing climate‐related risks and affecting water and food security, as well as natural ecosystems. However, future changes in southern African precipitation are uncertain, with climate models showing a wide range of responses from near‐term projections (2020–2040) to the end of the 21st century (2080–2100). Here, we assess the uncertainty in southern African precipitation change using five Ocean‐Atmosphere General Circulation single model initial‐condition large ensembles (30–50 ensemble members) and four emissions scenarios. We show that the main source of uncertainty in 21st Century projections of southern African precipitation is the internal climate variability. In addition, we find that differences between ensemble members in simulating future changes in the location of the Angola Low explain a large proportion (∼60%) of the uncertainty in precipitation change. Together, the internal variations in the large‐scale circulation over the Pacific Ocean and the Angola Low explain ∼64% of the uncertainty in southern African precipitation change. We suggest that a better understanding of the future evolutions of the southern African precipitation may be achieved by understanding better the model's ability to simulate the Angola Low and its effects on precipitation.

Summer Convective Precipitation Changes Over the Great Lakes Region Under a Warming Scenario

AbstractTo understand future summer precipitation changes over the Great Lakes Region (GLR), we performed an ensemble of regional climate simulations through the Pseudo‐Global Warming (PGW) approach. We found that different types of convective precipitation respond differently to the PGW signal. Isolated deep convection (IDC), usually concentrated in the southern domain, shows an increase in precipitation to the north of the GLR. Mesoscale convective systems (MCSs), usually concentrated upwind of the GLR, shift to the downwind region with increased precipitation. Thermodynamic variables such as convective available potential energy (CAPE) and convective inhibition energy (CIN) are found to increase across almost the entire studied domain, creating a potential environment more favorable for stronger convection systems and less favorable for weaker ones. Meanwhile, changes in the lifting condensation level (LCL) and level of free convection (LFC) show a strong correlation with variations in convective precipitation, highlighting the significance of these thermodynamic factors in controlling precipitation over the domain. Our results indicate that the decrease in LCL and LCF in areas with increased convective precipitation is mainly due to increased atmospheric moisture. In response to the prescribed warming perturbation, MCSs occur more frequently downwind, while localized IDCs exhibit more intense rain rates, longer durations, and larger rainfall area.

Impacts of projected future changes in precipitation on wastewater treatment plant influent volumes connected by combined sewer collection systems

Climate change induced precipitation changes can impact wastewater influent volumes and, in turn, the design and operation of wastewater treatment plants (WWTPs). As influent volumes approach or exceeds the design capacity of the WWTP, the likelihood of poor treatment or untreated discharges that result in environmental damage, increases. To date, there has been a lack of research analysing the impact changes in precipitation may have on influent volumes of WWTPs with combined sewerage systems. This study leverages data driven models of observed precipitation and influent volumes for 14 Irish WWTPs with combined sewerage systems, to project monthly wastewater influent volumes in 2041 – 2060 using Ireland’s most up-to-date high resolution multi-model RCM projections under RCPs 4.5 and 8.5. With changing monthly average daily precipitation, influent volumes for all the WWTPs demonstrated maximum decreases during summer months for both RCP 4.5 and RCP 8.5. Winter months showed increasing trends in influent volumes, particularly under RCP 8.5. The results indicate that with projected increase in high and very high precipitation days in Ireland (under CMIP5), the return periods for influent volumes that exceed the hydraulic capacity of 5 of the WWTPs will reduce. This work also presents a framework by which newer high-resolution downscaled climate models can be used to developed updated impact analysis of changing rainfall patterns on wastewater. It likely that wastewater treatment infrastructure will need to adapt to more intense precipitation to minimise the occurrence of combined sewer overflows.

Future Changes of Extreme Precipitation and Related Atmospheric Conditions in East Asia under Global Warming Projected in Large Ensemble Climate Prediction Data

Abstract Extreme precipitation is expected to pose a more severe threat to human society in the future. This work assessed the historical performance and future changes of extreme precipitation and related atmospheric conditions in a large ensemble climate prediction dataset, the database for Policy Decision-making for Future climate change (d4PDF), over East Asia. Compared with the TRMM and ERA5 datasets, the historical climate in d4PDF represents favorably the precipitation characteristics and the atmospheric conditions, although some differences are notable in the moisture, vertical motion, and cloud water fields. The future climate projection indicates that both the frequency and intensity of heavy precipitation events over East Asia increase compared with those in the present climate. However, when comparing the atmospheric conditions in the historical and future climates for the same precipitation intensity range, the future climate indicates smaller relatively humidity, weaker ascent, less cloud water content, and smaller temperature lapse rate, which negatively affect generating extreme precipitation events. The comparison of the precipitation intensity at the same amount of precipitable water between the historical and future climates indicates that extreme precipitation is weaker in the future, because of the more stabilized troposphere in the future. The general increase in extreme precipitation under future climate is primarily due to the enhanced increase in precipitable water in the higher temperature ranges, which counteracts the negative conditions of the stabilized troposphere.

Nonstationary multi-site design flood estimation and application to design flood regional composition analysis

Impacts of climate change and human activities may lead to changes in the spatiotemporal composition of the design flood as well as its size. Previous studies mainly focused on changes in design flood size, while there has been relatively little research on changes in its regional composition. In this study, a nonstationary multi-site design flood estimation method is developed, which is useful for the design flood regional composition analysis under nonstationary conditions. Dynamic copula models are first constructed to analyze the change in the joint distribution of the nonstationary multi-site flood variables with the consideration of the nonstationarity of the marginal distribution and copula structure parameters. Then the design flood combinations in multi-site for a specified design standard are calculated by comprehensively applying the equivalent reliability method, the expectation conditional and the most-likely conditional combination strategies, which considers the future precipitation change and design lifespan length impacts on the design flood. Finally, the uncertainty of the multi-site design flood estimation caused by the model parameters uncertainty is evaluated. A case study, based on the annual maximum 7-day (AM7) flood volume in the Yichang (YC) and Cuntan (CT) sites, is conducted to illustrate this method. Results show that flood quantiles in the YC and CT sites exhibit an increasing trend as the precipitation projections will increase in the future, but the flood quantiles in the YC site are less compared to the historical period because of the huge regulation and storage effect of the Three Gorges Reservoir. In addition, the design flood combination in the CT and YC sites are calculated and the CT design floods from the expectation combination strategy are bigger than those provided by the most-likely combination strategy.

Phase and Amplitude Changes in Rainfall Annual Cycle Over Global Land Monsoon Regions Under Global Warming

AbstractLand monsoon rainfall has a distinct annual cycle. Under global warming, whether the phase and amplitude of this annual cycle would be changed is still unclear. Here, a global investigation is conducted using 34 CMIP6 and 34 CMIP5 models under a high emission scenario. Seasonal delays would occur in the Southern Hemisphere (SH) American (3.43 days), Northern Hemisphere (NH) African (5.98 days) and SH African (3.76 days) monsoon regions, while no robust signal is found in other monsoon regions. Except NH American monsoon, amplitude is enhanced in all the monsoon regions. Compared to amplitude, the phase changes dominate the future changes of precipitation in the SH American, NH African and SH African monsoon regions. In these phase‐dominated regions, atmospheric energetic framework is proved to be reliable at regional scale and the enhanced effective atmospheric heat capacity is found to be the dominant factor.