Changes In Precipitation Extremes Research Articles

Evaluation of statistical downscaling techniques and projection of climate extremes in central Texas, USA

This study evaluates statistical downscaling techniques using different metrics and compares climate change signals and extreme precipitation and temperature changes under future climate change scenarios in the Bosque watershed, North-Central Texas. The study utilizes observed gridded Daymet data to assess the effectiveness of statistical downscaling techniques. It involves comparing the mean, the 90th percentile, 10th percentile, wet day frequency, and Cumulative Distribution Function (CDF) of climate model simulations before and after downscaling and the Daymet data during the historical period (1981–2005). Furthermore, the study analyzes changes in climate change signals, extreme precipitation, and temperature values under both near-future (2031–2060) and far-future (2070–2099) climate scenarios. The Ratio Delta method (DeltaSD) and Equi-Distant Quantile Mapping (EDQM) statistical downscaling techniques adjust the mean annual, the wet days frequency, the 90th and 10th percentiles, and the CDF of Global Climate Models (GCMs) simulations of historical precipitation and temperature. The downscaling techniques influenced the climate change signal and changes in extreme values in the future climate. When examining future climate projections produced using the DeltaSD method, we observe a more pronounced reduction in precipitation, while simulations generated through EDQM exhibit a higher frequency of heavy precipitation events (R10mm, R20mm) and consecutive dry days (CDD). It's worth noting that the uncertainties associated with the statistical downscaling techniques are relatively small and not statistically significant (≤0.05). In contrast, substantial and significant uncertainties arise from the choice of emission scenarios and the selection of driving GCMs. Across most climate change scenarios, there is a consistent trend towards increased temperatures and extreme temperature indices. The trend of extreme temperature indices shows variation following the choice of emission scenarios where a significant change in temperature extremes was detected under the RCP8.5 emission scenario.

Projected Change in Extreme Precipitation due to Global Warming in a Large Ensemble Climate Simulation

Projected Change in Extreme Precipitation due to Global Warming in a Large Ensemble Climate Simulation

Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability

Evaluating how extreme precipitation changes with climate is challenged by the paucity, brevity and inhomogeneity of observational records. Even when aggregating precipitation observations over large regions (obscuring potentially important spatial heterogeneity) the statistics describing extreme precipitation are often too uncertain to be of any practical value. Here we present an approach where a convolutional neural network (an artificial intelligence model) is trained on precipitation measurements from 10,000 stations to learn the spatial structure of the parameters of a generalised extreme value model, and the sensitivity of those parameters to the annual mean, global mean, surface temperature. The method is robust against the limitations of the observational record and avoids the short-comings of regional and global climate models in simulating the sensitivity of extreme precipitation to climate change. The maps of the sensitivity of extreme precipitation to climate change, on ~1.5 km × 1.5 km grids over North America, Europe, Australia and New Zealand, derived using the successfully trained convolutional neural network, show high spatial variability.

Exposure of the US population to extreme precipitation risk has increased due to climate change

The magnitude and frequency of extreme precipitation events in the early twenty-first century have already proven to be increasing at a rate more quickly than previously anticipated. Currently, the biggest consequence of the change in extreme precipitation is the lack of a climate-adjusted national standard taking into account these recent increases that could be used to prevent life and property loss from catastrophic precipitation-driven floods. Here, we address how severe the change in extreme precipitation compares against the current national standard for precipitation climatology (NOAA Atlas 14) and how much of the population is affected by the underestimation of this risk in the contiguous United States (CONUS). As a result, extreme precipitation in the early twenty-first century has outpaced our current national standard in half of CONUS, and the heavy precipitation events experienced recently are quickly becoming a “new normal”, which will increase in severity and frequency in a continually changing climate. Over three-quarters of the U.S. population will likely experience this new normal occurrence of extreme precipitation. As much as one-third of the population is expected to experience the current definition of a 1-in-100-year storm as often as three times in their lifetime. Additionally, the current precipitation standards for designing transportation infrastructure and urban stormwater drainage systems that are built upon Atlas 14 may be insufficient to protect the public's safety and personal/community property from severe flooding. Areas where flood risk is mitigated by operating hydraulic and adaptation structures urgently need to assess the impact of the increased-hourly extreme precipitation and reevaluate their applicable operation rules. Understanding and predicting patterns and the likelihood of short-duration heavy precipitation would be beneficial in preparing for severe precipitation-driven disasters, such as flash floods and landslides, which would happen more frequently in a changing climate. Following the results of this analysis, accelerating the development and dissemination of the next generation of the national standard that has been climatically adjusted to adapt to the new normal is strongly recommended.

Evaluation of precipitation extremes over the Tibetan plateau using the NASA global daily downscaled datasets NEX-GDDP-CMIP6

The utility of statistically downscaled data is in its provision of detailed, high-resolution insights, surpassing global models, which is essential for precise assessments of climate impact. This study delves into the changes in extreme precipitation across the Tibetan Plateau (TP) and its five subregions, thereby deepening our understanding of climate impacts in the region. Employing NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6), which are bias-corrected and statistically downscaled, in conjunction with three gauge-based datasets (CN05.1, CMFD, GMFD) spanning from 1979 to 2014, this study conducts an extensive analysis using eight extreme precipitation indices in the TP. The findings reveal significant spatial disparities in extreme precipitation, with the southern subregion experiencing a significant increase while the two northern subregions show statistically insignificant changes. NEX-GDDP-CMIP6 performs best in the eastern subregion but fails to capture extreme precipitation characteristics in the southern and western subregions. Averaging over the whole period of 1979–2014 (climatology), the NEX-GDDP-CMIP6 dataset performs better on the intensity and persistence indices than on the absolute and relative threshold indices. NEX-GDDP-CMIP6's performance on climate trends is significantly below that of climatology. The NEX-GDDP-CMIP6 dataset has limitations over the TP because of its reliance on the underperforming GMFD reference data. This study provides a comprehensive understanding of the variability of extreme precipitation and disparities among different subregions of the TP, laying a robust scientific groundwork for climate impact studies and future projection in this region.

Extreme precipitation causes divergent responses of root respiration to nitrogen enrichment in an alpine meadow

Grassland roots are fundamental to obtain the most limiting soil water and nitrogen (N) resources. However, this natural pattern could be significantly changed by recent co-occurrence of N deposition and extreme precipitations, likely with complex interactions on grassland root production and respiration. Despite this nonlinearity, we still know little about how extreme precipitation change nonlinearly regulates the responses of root respiration to N enrichment. Here, we conducted a 6-year experiment of N addition in an alpine meadow, coincidently experiencing extreme precipitations among experimental years. Our results demonstrated that root respiration showed divergent responses to N addition along with extreme precipitation changes among years. Under normal rainfall year, root respiration was significantly stimulated by N addition, whereas it was depressed under high or low water. Moreover, we revealed that both root biomass and traits (i.e. specific root length) were critical mechanisms in affecting root respiration response, but their relative importance changed with water condition. For example, specific root length and specific root respiration were more dominant than root biomass in determining root respiration response under low water, or vice versa. Overall, this study comprehensively reveals the nonlinearity of root respiration responses to the interactions of N enrichment and extreme water change. These new findings help to reconcile previously conflicting results that obtain in a specific episode of water gradient, with important implications for understanding grassland belowground carbon dynamics in facing combined N deposition and extreme precipitation events.

Assessing the hillslope-channel contributions to the catchment sediment balance under climate change

To get a full understanding of the impacts of global change on the catchment-scale sediment balance, models are needed that combine hillslope soil erosion processes with channel morphodynamics. Here we present a modification to the SPHY-MMF model that includes a novel channel morphodynamics module, which determines erosion and deposition in rills and channels. We applied the model to a Mediterranean study area in southeast Spain, in which we show that channel erosion contributes substantially (35%–40%) to the total sediment yield, highlighting the importance of accounting for channel erosion in catchment-scale sediment budget estimations. The climate change scenarios show that the different erosional processes (i.e. sheet, rill, channel) are projected to decrease or increase, depending on the projected change in annual and extreme precipitation. From this we conclude that interactions between different erosional and depositional processes should be considered when studying the impact of global change on the catchment-scale sediment balance.

Differential responses of soil extracellular enzyme activity and stoichiometry to precipitation changes in a poplar plantation

Changes in precipitation patterns can significantly affect belowground processes. Although soil extracellular enzymes play a vital role in several biogeochemical processes, our knowledge of how precipitation changes affect soil extracellular enzyme activity (EEA) and stoichiometry remains insufficient. In this study, we investigated the activities of C-acquiring enzyme (β-1,4-glucosidase), N-acquiring enzymes (β-N-acetylglucosaminidase and leucine aminopeptidase), and P-acquiring enzyme (acid phosphatase) under different precipitation scenarios [ambient precipitation (CK), 30% decrease in precipitation (moderate DPT), 50% decrease in precipitation (extreme DPT), 30% increase in precipitation (moderate IPT), and 50% increase in precipitation (extreme IPT)] in a poplar plantation. We found soil EEA exhibited more pronounced increases to moderate IPT compared to moderate DPT (positive asymmetry), the opposite trend (negative asymmetry) was observed under extreme precipitation; whereas soil EEA C:N:P stoichiometry exhibited negative asymmetry at moderate precipitation changes, and exhibited positive asymmetry at extreme precipitation changes. Under moderate precipitation changes, the asymmetry of soil EEA was mainly regulated by asymmetries of respective microbial biomass and litter mass; the asymmetry of soil EEA stoichiometry was mainly regulated by asymmetries of respective soil stoichiometric ratios and litter mass. Furthermore, under extreme precipitation changes, the asymmetries of soil EEA and stoichiometry were best explained by the asymmetry of soil moisture. Our results provide the first evidence of double asymmetric responses of soil EEA and stoichiometry to precipitation changes and highlight the need to consider this asymmetry when modeling the dynamics of biogeochemical cycling in forest ecosystems.

The increase in extreme precipitation and its proportion over global land

Changes in extreme precipitation (EP) have a significant impact on the ecology and sustainable development of most regions, especially when considered against a backdrop of global warming. Using trend analysis and other methods, we analyze the changes in nine extreme precipitation indices (EPIs), looking at the proportion of EP in total precipitation from the perspectives of intensity, frequency, and duration. We also utilize a geo-detector method to investigate the impacts of 15 climate factors on EPIs. Our results indicate that although EP and their proportions show an overall increasing trend on a global scale, they had undergone a process of initial decrease followed by increase over the past approximately 70 years. And this overall global increases are charged by the significant increases happened in Northern Hemisphere and western of South America, especially EP intensity and frequency indices, and their proportion. Whereas CDD shows an opposite trend in spatial distribution, the CWD is decreasing globally. The EPIs and their proportions are all highest in the low elevation belt (≤2000 m), followed by the high elevation belt (≥4000 m), and lowest in the mid-elevation belts (2000-4000 m). Most of them have returned to or exceeded the levels of the mid-20th century in all three elevation belts. Only CWD shows a decreasing trend, which indicates that more EP amounts are occurring with less precipitation duration. In some arid areas, this makes it possible that a short period of precipitation may complete the total amount of precipitation for that year. Global temperature is proven to be the most important factor influencing the EPI globally, and followed by the temperature of each region. And the regions of high latitudes of the Northern Hemisphere and Antarctica which are greatly influenced by global temperature demonstrate the Polar amplification effect. The East Atlantic-West Russia Pattern, Atlantic Multi-Decadal Oscillation, and El Nino-Southern Oscillation indices are the secondly important factors leading to changes in the proportion of global EP. At the same time, we also find that the influence of non-monsoon factors on EP is gradually strengthening. This study clarifies changes in EP globally and in various regions, which will help provide a scientific reference for preventing disasters caused by extreme climate change.

Exploring changes of precipitation extremes under climate change through global variable-resolution modeling

Understanding the responses of precipitation extremes to global climate change remains limited owing to their poor representations in models and complicated interactions with multi-scale systems. Here we take the record-breaking precipitation over China in 2021 as an example, and study its changes under three different climate scenarios through a developed pseudo-global-warming (PGW) experimental framework with 60–3 km variable-resolution global ensemble modeling. Compared to the present climate, the precipitation extreme under a warmer (cooler) climate increased (decreased) in intensity, coverage, and total amount at a range of 24.3%–37.8% (18.7%–56.1%). With the help of the proposed PGW experimental framework, we further reveal the impacts of the multi-scale system interactions in climate change on the precipitation extreme. Under the warmer climate, large-scale water vapor transport converged from double typhoons and the subtropical high marched into central China, enhancing the convective energy and instability on the leading edge of the transport belt. As a result, the mesoscale convective system (MCS) that directly contributed to the precipitation extreme became stronger than that in the present climate. On the contrary, the cooler climate displayed opposite changing characteristics relative to the warmer climate, ranging from the large-scale systems to local environments and to the MCS. In summary, our study provides a promising approach to scientifically assess the response of precipitation extremes to climate change, making it feasible to perform ensemble simulations while investigating the multi-scale system interactions over the globe.

MIROC6 Large Ensemble (MIROC6-LE): experimental design and initial analyses

Abstract. Single model initial-condition large ensembles (LEs) are a useful approach to understand the roles of forced responses and internal variability in historical and future climate change. Here, we produce one of the largest ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). The total experimental period of MIROC6-LE is longer than 76 000 years. MIROC6-LE consists of a long preindustrial control run, 50-member historical simulations, 8 single forcing historical experiments with 10 or 50 members, 5 future scenario experiments with 50 members and 3 single forcing future experiments with 50 members. Here, we describe the experimental design. The output data of most of the experiments are freely available to the public. This dataset would be useful to a wide range of research communities. We also demonstrate some examples of initial analyses. Specifically, we confirm that the linear additivity of the forcing-response relationship holds for the 1850–2020 trends of the annual mean values and extreme indices of surface air temperature and precipitation by analyzing historical fully forced runs and the sum of single forced historical runs. To isolate historical anthropogenic signals of annual mean and extreme temperature for 2000–2020 relative to 1850–1900, ensemble sizes of 4 and 15, respectively, are sufficient in most of the world. Historical anthropogenic signals of annual mean and extreme precipitation are significant with the 50-member ensembles in 76 % and 69 % of the world, respectively. Fourteen members are sufficient to examine differences in changes in annual mean values and extreme indices of temperature and precipitation between the shared socioeconomic pathways (ssp), ssp585 and ssp126, in most of the world. Ensembles larger than 50 members are desirable for investigations of differences in annual mean and extreme precipitation changes between ssp126 and ssp119. Historical and future changes in internal variability, represented by departures from the ensemble mean, are analyzed with a focus on the El Niño/Southern Oscillation (ENSO) and global annual mean temperature and precipitation. An ensemble size of 31 is large enough to detect ENSO intensification from preindustrial conditions to 1951–2000, from 1951–2000 to 2051–2100 in all future experiments, and from low- to high-emission future scenario experiments. The single forcing historical experiments with 27 members can isolate ENSO intensification due to anthropogenic greenhouse gas and aerosol forcings. Future changes in the global mean temperature variability are discernible with 23 members under all future experiments, while 50 members are not sufficient for detecting changes in the global mean precipitation variability in ssp119 and ssp126. We also confirm that these temperature and precipitation variabilities are not precisely analyzed when detrended anomalies from the long-term averages are used due to interannual climate responses to the historical natural forcing, which highlights the importance of large ensembles for assessing internal variability.

Climatological trends of mean and extreme daily precipitation in Arizona (USA)

Precipitation in Arizona, USA, represents a fundamental resource for the needs of agriculture, people, and the environment. However, due to the risk associated with flash flooding, extreme precipitation also constitutes a natural hazard. Available research on precipitation in Arizona has been limited in scope and focus and there currently exists no comprehensive assessment of statewide, historical, mean, and extreme precipitation. We use daily precipitation records from 43 Global Historical Climate Network daily (GHCNd) weather stations to examine trends in mean and extreme precipitation across Arizona from 1950 to 2020. We use a suite of standardized precipitation indices and explore the statistical significance of historical changes at the annual, monthly, and seasonal scales. Our analysis returns a motley collection of results displaying a great degree of spatial variability and sensitivity to the temporal scale of analysis. Mean total precipitation at the statewide scale underwent a small decreasing trend of 0.15 mm year−1, with considerable interannual variability being a dominant feature. The majority of stations experienced no statistically significant change in extreme precipitation at the annual scale. Monsoon season mean precipitation followed a similar pattern to annual means, with an overall reduction of 0.14 mm year−1 across the state. One fourth of stations, all located at elevations lower than 650 m, recorded decreases in monsoon precipitation intensity. Winter season analysis presents a different picture, displaying a positive statewide trend of 0.11 mm year−1 in mean precipitation. One tenth of stations recorded increases in extreme precipitation intensity and decreases in the number of consecutive dry days. Results of our observational analysis indicate a lack of a clear consensus on the climatological trends of mean/extreme precipitation in Arizona during the study period. Our findings are in contrast to those from other similarly arid areas across the world and highlight the role of regional differences in modulating the potential hydrometeorological impacts associated with climate change. However, while we do not find widespread, significant, modification in the spectrum of precipitation changes examined, the hydrologic system of the state has been – and will continue to be - impacted by the ongoing temperature increase associated with the buildup of greenhouse gases and continued population growth.

Future projections of extreme precipitation in Tropical America and Panama under global warming based on 150‐year continuous simulations using 20‐km and 60‐km atmospheric general circulation models

AbstractThis study explores future changes in extreme precipitation across tropical America and Panama using 150‐year continuous experiments run on the Meteorological Research Institute atmospheric general circulation model at horizontal resolutions of 20 km (AGCM20) and 60 km (AGCM60). Tropical America is a warming hotspot where the mean seasonal precipitation amounts are decreasing, but annual maximum daily precipitation amounts are following an increasing trend. In Panama, future climates simulated using AGCM20 and AGCM60 show an increase in December–January–February (DJF) precipitation over eastern Panama, and a decrease in June–July–August (JJA) precipitation over western areas near the Pacific coast. Annual maximum daily precipitation increases on the eastern side of the country in both models. The mean annual maximum daily precipitation for tropical America shows decadal oscillations, similar to the global mean but with smaller amplitude variations. The mean annual maximum daily precipitation for Panama shows large oscillations, even for the 10‐year running mean. For tropical America, the mean annual maximum daily precipitation has a linear relationship with surface air temperature that is independent of the Representative Concentration Pathway scenarios, but this is not the case for Panama. Temporal correlations between the tropical America mean annual maximum daily precipitation and annual mean surface air temperature in the historical experiments have a geographical distribution similar to that of the sea surface temperature anomalies during negative El Niño/Southern Oscillation events, but not in the future experiments. The correlation between the Panama mean annual maximum daily precipitation and surface air temperature in both the historical and future experiments has a geographical distribution similar to that of the temporal correlation for the tropical America mean in the historical experiment. The annual maximum daily precipitation in Panama is projected to increase during negative El Niño events in the future, as observed in the historical climate.

Assessing Global Landslide Casualty Risk Under Moderate Climate Change Based on Multiple GCM Projections

Extreme precipitation-induced landslide events are projected to increase under climate change, which poses a serious threat to human lives and property. In this study, a global-scale landslide risk assessment model was established using global landslide data, by considering landslide hazard, exposure, and vulnerability. The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes. The results show that compared to the 30-year period from 1971 to 2000, the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7% and 10%, respectively, in the future 30-year periods of 2031–2060 and 2066–2095. The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380, respectively (with growth rates of 140% and 160%), during the 2031–2060 and 2066–2095 periods under the SSP2-4.5 scenario. The top 10 countries with the highest casualty risk of landslides are China, Afghanistan, India, the Philippines, Indonesia, Rwanda, Turkey, Nepal, Guatemala, and Brazil, 60% of which are located in Asia. The frequency and intensity of extreme precipitation will increase under climate change, which will lead to an increase in casualties from landslides in mountainous areas globally, and this risk should be taken seriously. The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change, which still has uncertainty in terms of outcomes, and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk.

Effects of Urbanization on Changes in Precipitation Extremes in Guangdong-Hong Kong-Macao Greater Bay Area, China

Complex interaction between urbanization and climate change has been showing significant impacts on natural and human ecosystems. Increasing urban flooding and waterlogging are associated with urbanization. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) experiences a rapid and extensive urbanization, leading to intensified land use and cover changes. Concurrently, the frequent occurrence of extreme precipitation events pose great challenges of urban flood control and water resource management to GBA. This research statistically analyzes the spatiotemporal evolution characteristics of precipitation extremes from 1979–2018 which relates to the urbanization in GBA using various statistical methods including the Mann-Kendall test, bivariate Moran’s test, and Spearman correlation analyses. The findings indicate that the impervious surface area in GBA exhibited a nonlinear growth trend from 1985–2018, particularly concentrated in the five major cities, i.e., Guangzhou, Foshan, Dongguan, Shenzhen, and Zhongshan. GBA urbanization can be categorized into three stages including Stage I (pre-1990, no urbanization), Stage II (1991–2009, rapid urbanization), and Stage III (2010–2018, slow urbanization). Compared to cities with low urbanization, the highly urbanized areas of GBA, including Guangzhou, Foshan, Zhongshan, and Dongguan, show statistically significant increases in precipitation extremes. The increasing trends of seven extreme precipitation indices show significant positive, spatiotemporal correlations with the change rate of urbanization in GBA. Moreover, the influence of urbanization on precipitation extremes in highly urbanized regions of the GBA is progressively strengthened along with urban development. During the stage of slow urbanization, urbanization contributes to 56.13% of increase in annual precipitation totals in GBA, and its contribution to increases in precipitation extremes ranges from 20–80%.

Future extreme precipitation changes in the South Asian Summer Monsoon Dominance Region

This study aims to investigate the future changes in precipitation extremes across the South Asian summer monsoon domain using the latest CMIP6 global climate model (GCM) outputs under four SSP-RCPs for the near future (2031–2065) and the far future (2066–2100). The models were bias-corrected using nonparametric quantile transformation based on the smoothing spline technique. Future analysis was done in a more robust and systematic way by using seasonal-based as well as regional-based approaches. Two seasons, pre-monsoon from March to May (MAM) and monsoon from June to September (JJAS) were considered for seasonal assessment. The study area was divided into subzones based on Köppen-Geiger's climate structure for regional assessment. Five extreme precipitation indices, which describe extreme precipitation events, R95pTOT, Rx1day, Rx5day, PRCPTOT and SDII from Expert Team on Climate Change Detection and Indices (ETCCDI), were applied. The evaluation results reveal that the performance of the bias correction method differs based on the extreme precipitation index, climate zone, and season. Based on the model agreement, a negative shift in the precipitation extremes in the future will be dominant during the pre-monsoon season, particularly in the subzones of Tropical rainforest (southern regions of Sri Lanka), Tropical monsoon (Western Ghats of India, coastal regions of Myanmar, regions of Bangladesh), Tropical savannah (regions of Myanmar), and Arid steppe hot (southern regions of India, central parts of Myanmar). During monsoon season, both negative and positive shifts will prevail in the subzones, and the precipitation extremes during the far future will be more intensified than near future in most of the subzones. Additionally, the shifts will tend to decline from SSP1–2.6 to SSP5–8.5 in the near future and increment from SSP1–2.6 to SSP5–8.5 in the far future.

Derivation of nonstationary rainfall intensity-duration-frequency curves considering the impacts of climate change and urbanization

Urban infrastructure traditionally relies on stationary rainfall intensity-duration-frequency (IDF) curves. However, this assumption is challenged by climate change and urbanization. Many studies tried to update IDF using time covariate which lacks physical significance. More importantly, the stationary (ST) design method is not applicable for nonstationary (NS) design where the distributions of extreme precipitation change over time. For the annual maximum precipitation (AMP) in Beijing, we utilized local factors (urbanization and temperature) and global factors (ENSO and EASM etc.) to develop NS models, with the average annual reliability method first employed to update the IDF curves. Short-duration (shorter than 6-h) AMP of most stations show upward trends, whereas the AMP with longer durations exhibits downward trends. The NS modeling reveals that the 18-h AMPs is mainly affected by global processes (ENSO and EASM). The predictive accuracy of the optimal NS model outperforms ST model by a remarkable 219% during the validation period. In addition, the ST design rainfall tends to overestimate rainfall for durations longer than 12-h. Interestingly, the gap between NS and ST design uncertainties diminishes as duration/return period expands. The above findings provide new insights about impacts of local and global physical processes on the variation of extreme rainfall.

Predictive Study on Extreme Precipitation Trends in Henan and Their Impact on Population Exposure

This study employs precipitation data sets from historical trials on 20 CMIP6 global climate models and four shared socioeconomic pathway scenario trials (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to predict trends in extreme precipitation changes in Henan Province quantitatively, while ascertaining the risk of population exposure to extreme precipitation in this area. The capacity of the CMIP6 models to simulate extreme precipitation indices from 1985 to 2014 is assessed using CN05.1 daily precipitation observational data. The correlation coefficients of the multi-model ensemble median’s simulation of the extreme precipitation indices are approximately 0.8, with a standard deviation ratio closer to 1 compared with the single models, demonstrating superior modeling ability. Analyses using the multi-model ensemble median demonstrate an overall increase in the total amount, frequency, and intensity of extreme precipitation in Henan throughout this century, particularly in its southern regions; in the mid-century high-emission scenario (SSP5-8.5), the maximum increase in annual total precipitation exceeds 150 mm, and it can be over 250 mm in the late-century period. For the entire province, the maximum five-day precipitation increase relative to the historical period is nearly 25 mm in the late-century SSP5-8.5 scenario. The spatiotemporal concentration of precipitation will significantly increase, heightening the risk of flood disasters. Comparative analysis reveals that, under the same population prediction, the total population exposure will be higher in high radiative forcing scenarios than in low radiative forcing scenarios, especially in Kaifeng City, where the total population exposure in SSP1 and SSP5-8.5 exceeds that in SSP1-2.6 by 2 million person-days. However, in the same radiative forcing scenario, the total population exposure in the development pathway dominated by traditional fossil fuels (SSP5) will not be significantly higher than that in the sustainable development pathway (SSP1), indicating that population activity in this century will not be the main contributor to changes in total exposure. Overall, for Henan, in the same population forecast scenario, population exposure to extreme precipitation will gradually rise with global warming.

Linking Historical and Projected Trends in Extreme Precipitation with Cumulative Carbon Dioxide Emissions

Extreme weather events are expected to increase in frequency and intensity in response to higher global temperatures, augmenting societal exposure to these events. While the magnitude of projected changes in extremes varies considerably among future emission scenarios, a large part of this uncertainty is driven by the choice of scenario, rather than by the climate response to a particular emission scenario. A growing body of research has identified robust linear relationships between climate changes and cumulative carbon emissions; for global average temperature change, this relationship is known as the transient climate response to cumulative carbon emissions (TCRE). Extensions of the TCRE framework to other variables, such as regional and seasonal temperature and precipitation changes, have also shown to be effective, raising the possibility that changes in weather extremes could be linked to cumulative carbon dioxide (CO2) emissions. Here, we estimate changes in historical and projected trends in one-day (Rx1day) and five-day maximum precipitation (Rx5day) events as a function of cumulative carbon emissions across a range of future emission scenarios and global climate models. Our results show that median Rx1day and Rx5day generally increases linearly with increasing cumulative emissions, consistent with studies that have previously employed the TCRE framework to estimate changes in precipitation extremes, as well as other climate indicators. Overall, we show that a linear response to cumulative CO2 emissions is a good approximation for both historical and future trends in precipitation extremes.

Identifying Robust Changes of Extreme Precipitation in Japan From Large Ensemble 5‐km‐Grid Regional Experiments for 4K Warming Scenario

AbstractTo evaluate the impacts of global warming on local‐scale extreme precipitation in Japan, 720‐year ensemble dynamical downscaling is conducted by a regional climate model with a 5 km grid spacing. Our dynamical downscaling is based on the large ensemble data set called the database for Policy Decision making for Future climate change (d4PDF). Comparing the historical and 4K warming climates, the annual maximum daily and hourly precipitation are enhanced over Japan due to global warming. The increasing rate of annual maximum daily precipitation is larger over the coast of the Pacific Ocean in eastern and western Japan and the northern parts of Japan. The periods in which the extreme daily precipitation increases in each region depend on the movement of the Baiu front and the number of typhoons from June through September. The 50‐year return value of the maximum 24‐hr land precipitation within 500 km of the typhoon center is 960 mm/24hr in the 5 km experiment. This value is larger by 1.87 times than that of the 20 km experiment. The rate of future increase in the 50‐year return value related to typhoons is 1.30 relative to the present values for the 5 km experiments. The local‐scale quasi‐stationary band‐shaped precipitation system, which is called senjo‐kousuitai in Japanese, sometimes brings local‐scale heavy precipitation to Japan. The senjo‐kousuitai is well reproduced in the 5 km experiments and shows increases in frequency and intensity under the 4K warming condition. This study also discusses the application of our 5 km experiments to hydrological fields.