Changes In Precipitation Extremes Research Articles

Evaluation and projection of extreme precipitation using CMIP6 model simulations in the Yellow River Basin

Abstract The capabilities of 23 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 were evaluated for six extreme precipitation indices from 1961 to 2010 using interannual variability and Taylor skill scores in the Yellow River Basin and its eight subregions. The temporal variations and spatial distributions of extreme precipitation indices were projected from 2021 to 2050 under the shared socioeconomic pathway scenarios (SSP2–4.5 and SSP5–8.5). The results show that most GCMs perform well in simulating extreme values (1-day maximum precipitation (RX1day) and 5-day maximum precipitation (RX5day)), duration (consecutive dry days), and intensity index (simple daily intensity index (SDII)), and perform poor in simulating the threshold indices (precipitation on very wet days (R95p) and number of heavy precipitation days (R10mm)). The projected changes in extreme precipitation indicate that under the SSP2-4.5 scenario, future extreme precipitation will increase by 15.7% (RX1day), 15.8% (RX5day), 30.3% (R95p), 1d (R10mm), and 6.6% (SDII), respectively, decrease by 2.1d (CDD). The aforementioned changes are further enhanced under the SSP5-8.5 scenario. Extreme precipitation changes widely in Hekou Town to Longmen, in the northeastern part of the region from Longmen to Sanmenxia, below Huayuankou, and in the interflow basin.

Projected Changes in Mean and Extreme Precipitation over Northern Mexico

Abstract Northern Mexico is home to more than 32 million people and is of significant agricultural and economic importance for the country. The region includes three distinct hydroclimatic regions, all of which regularly experience severe dryness and flooding and are highly susceptible to future changes in precipitation. To date, little work has been done to characterize future trends in either mean or extreme precipitation over northern Mexico. To fill this gap, we investigate projected precipitation trends over the region in the NA-CORDEX ensemble of dynamically downscaled simulations. We first verify that these simulations accurately reproduce observed precipitation over northern Mexico, as derived from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) product, demonstrating that the NA-CORDEX ensemble is appropriate for studying precipitation trends over the region. By the end of the century, simulations forced with a high-emissions scenario project that both mean and extreme precipitation will decrease to the west and increase to the east of the Sierra Madre highlands, decreasing the zonal gradient in precipitation. We also find that the North American monsoon, which is responsible for a substantial fraction of the precipitation over the region, is likely to start later and last approximately three weeks longer. The frequency of extreme precipitation events is expected to double throughout the region, exacerbating the flood risk for vulnerable communities in northern Mexico. Collectively, these results suggest that the extreme precipitation-related dangers that the region faces, such as flooding, will increase significantly by the end of the century, with implications for the agricultural sector, economy, and infrastructure. Significance Statement Northern Mexico regularly experiences severe flooding and its important agricultural sector can be heavily impacted by variations in precipitation. Using high-resolution climate model simulations that have been tested against observations, we find that these hydroclimate extremes are likely to be exacerbated in a warming climate; the dry (wet) season is projected to receive significantly less (more) precipitation (approximately ±10% by the end of the century). Simulations suggest that some of the changes in precipitation over the region can be related to the North American monsoon, with the monsoon starting later in the year and lasting several weeks longer. Our results also suggest that the frequency of extreme precipitation will increase, although this increase is smaller than that projected for other regions, with the strongest storms becoming 20% more frequent per degree of warming. These results suggest that this region may experience significant changes to its hydroclimate through the end of the century that will require significant resilience planning.

Projected changes in extreme daily precipitation linked to changes in precipitable water and vertical velocity in CMIP6 models

Understanding the drivers of precipitation and their changes in a non-stationary climate is crucial for effective climate adaptation and water resource management, as it helps us anticipate and respond to shifting precipitation patterns and their impacts. Here, analysing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) we show that the conditional probability of extreme daily precipitation given joint extremes of two drivers (precipitable water and vertical velocity) will be stable in a 3 °C warmer future. Consistent with earlier work, we find that the near-global increase in precipitable water (thermodynamic influence) is the baseline for changes in extreme precipitation, which are modulated by changes in vertical velocity (dynamic influence). Thus, in regions where vertical velocity increases, the effect of the two drivers is additive and their changes contribute to an increase in extreme precipitation. The changes of the two drivers are opposite where vertical velocity decreases, resulting in only small increases in extreme precipitation or even a decrease. Furthermore, we reveal that there are moderate changes in the dependence between the drivers, which are larger over the ocean than over landmasses, but they contribute only little to the overall changes in extreme precipitation. We conclude that the use of two very simple drivers that are readily available from climate models can be of great utility for evaluating precipitation extremes in models and understanding their projected changes.

Contributions of Tropical Cyclones and Atmospheric Rivers to Extreme Precipitation Trends Over the Northeast US

AbstractThe Northeast United States (NEUS) has faced the most rapidly increasing occurrences of extreme precipitation within the US in the past few decades. Understanding the physics leading to long‐term trends in regional extreme precipitation is essential but the progress is limited partially by the horizontal resolution of climate models. The latest fully coupled 25‐km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) simulations provide a good opportunity to study changes in regional extreme precipitation and the relevant physical processes. Here, we focus on the contributions of changes in synoptic‐scale events, including atmospheric rivers (AR) and tropical cyclone (TC)‐related events, to the trend of extreme precipitation in the fall season over the Northeast US in both the recent past and future projections using the 25‐km GFDL‐SPEAR. In observations, increasing extreme precipitation over the NEUS since the 1990s is mainly linked to TC‐related events, especially those undergoing extratropical transitions. In the future, both AR‐related and TC‐related extreme precipitation over the NEUS are projected to increase, even though the numbers of TCs in the North Atlantic are projected to decrease in the SPEAR simulations using the SSP5‐8.5 projection of future radiative forcing. Factors such as enhancing TC intensity, strengthening TC‐related precipitation, and/or westward shift in Atlantic TC tracks may offset the influence of declining Atlantic TC numbers in the model projections, leading to more frequent TC‐related extreme precipitation over the NEUS.

Landscapes on the edge: River intermittency in a warming world

Sediment transport in rivers is not steady through time. Highly intermittent river systems, which only transport bedload during the most significant flow events, are particularly sensitive to changes in climate and precipitation patterns. People and landscapes can be vulnerable to fluvial processes, and quantifying river intermittency is critical for assessing landscape response to projected changes in precipitation extremes due to climate change. We generated new constraints on recent to modern fluvial intermittency factors—the frequency at which bedload is mobilized in a river—based on field measurements in the Corinth Rift, Greece, and Holocene sediment accumulation rates. Results reveal some of the lowest documented intermittency factors to date, showing Mediterranean rivers can transport an entire annual sediment load in a rare storm event. Coupling intermittency calculations with historical flood and precipitation data indicates these rivers transport bedload during one storm every ∼4 yr, associated with rainfall >50 mm/d, and subsequent floods; this hydroclimate is typical across the Mediterranean region. Furthermore, climate models predict precipitation extremes will increase across Europe, and the frequency of events that surpass thresholds of sediment transport will increase significantly, potentially causing sediment loads to double by 2100 CE. As the area of arid land likely to host intermittent rivers also increases, sensitive landscapes are on the edge of significant geomorphic change, driven by global warming.

Climate change impacts on rainfall intensity–duration–frequency curves in local scale catchments

The increasing intensity and frequency of rainfall events, a critical aspect of climate change, pose significant challenges in the construction of intensity–duration–frequency (IDF) curves for climate projection. These curves are crucial for infrastructure development, but the non-stationarity of extreme rainfall raises concerns about their adequacy under future climate conditions. This research addresses these challenges by investigating the reasons behind the IPCC climate report’s evidence about the validity that rainfall follows the Clausius-Clapeyron (CC) relationship, which suggests a 7% increase in precipitation per 1 °C increase in temperature. Our study provides guidelines for adjusting IDF curves in the future, considering both current and future climates. We calculate extreme precipitation changes and scaling factors for small urban catchments in Barranquilla, Colombia, a tropical region, using the bootstrapping method. This reveals the occurrence of a sub-CC relationship, suggesting that the generalized 7% figure may not be universally applicable. In contrast, our comparative analysis with Illinois, USA, an inland city in the north temperate zone, shows adherence to the CC relationship. This emphasizes the need for local parameter calculations rather than relying solely on the generalized 7% figure.

Space–Time Characterization of Extreme Precipitation Indices for the Semiarid Region of Brazil

Various indices of climate variability and extremes are extensively employed to characterize potential effects of climate change. Particularly, the semiarid region of Brazil is influenced by adverse effects of these changes, especially in terms of precipitation. In this context, the main objective of the present study was to characterize the regional trends of extreme precipitation indices in the semiarid region of Brazil (SAB), using daily precipitation data from the IMERG V06 product, spanning the period from 1 January 2001 to 31 December 2020. Twelve extreme precipitation indices were considered, which were estimated annually, and their spatial and temporal trends were subsequently analyzed using the nonparametric Mann–Kendall test and Sen’s slope. The analysis revealed that the peripheral areas of the SAB, especially in the northwest and extreme south regions, exhibited higher intensity and frequency of extreme precipitation events compared to the central portion of the area. However, a negative trend in event intensity was noted in the north, while positive trends were identified in the south. The frequency of extreme events showed a predominance of negative trends across most of the region, with an increase in consecutive dry days particularly throughout the western SAB. The average total precipitation index was above 1000 mm in the north of the SAB, whereas in the central region, the precipitation averages were predominantly below 600 mm, with rainfall intensity values ranging between 6 and 10 mm/day. Over the span of 20 years, the region underwent an average of 40 consecutive dry days in certain localities. A negative trend was observed in most of the indices, indicating a reduction in precipitation intensity in future decades, with variations in some indices. The dry years observed towards the end of the analyzed period likely contributed to the observed negative trends in the majority of extreme precipitation indices. Such trends directly impact the intensity and frequency of extreme weather events in the SAB. The study is important for highlighting and considering the impacts of changes in precipitation extremes in the semiarid region of Brazil. Based on the obtained results, we advocate the implementation of public policies to address future challenges, such as incorporating adaptations in water resource management, sustainable agricultural practices, and planning for urban and rural areas.

State-of-the-art climate models reduce dominant dynamical uncertainty in projections of extreme precipitation

Extreme precipitation can lead to severe environmental and economic impacts. Thus, future changes in extreme precipitation and their uncertainties are of major interest. Changes in extreme precipitation can be decomposed into thermodynamic (temperature-related) and dynamic (vertical velocity related) contributions with a scaling approach for extreme precipitation. Applying this approach to the global climate model ensembles CMIP5 and CMIP6, we decompose projection uncertainties of extremes in daily precipitation into uncertainties of thermodynamic and dynamic changes. We analyze regional patterns of the total uncertainties in extreme precipitation projections, as well as the thermodynamic and dynamic contributions to these uncertainties. Total uncertainties relative to the projected multi model mean are dominated by the dynamical contributions, and are large over the tropics and subtropics, but smaller over the high and mid-latitudes. Uncertainties in the thermodynamic contribution are generally small. From CMIP5 to CMIP6, uncertainties in thermodynamic and dynamic changes are slightly reduced in the high and mid-latitudes, while there is a substantial reduction of the uncertainties of the dynamic changes in the tropics and subtropics.

Using machine learning to analyze the changes in extreme precipitation in southern China

Convolutional neural network (CNN) is applied to identify the extreme precipitation events (EPEs) in southern China and the physical contributions are quantified for the changes in extreme precipitation in recent decades. The CNN correctly identifies about 96% of the observed EPEs based on the given large-scale atmospheric circulation. The discrimination made by the neural network is revealed by using the layer-wise relevance propagation method. The CNN is inclined to classify the circulation pattern as an extreme precipitation circulation pattern (EPCP), when a deep cyclonic anomaly and intense horizontal pressure gradient appear over southern China at the lower and middle troposphere. The extreme precipitation amount decreases during April and May (AM) and increases in June to August (JJA) after the early-1990s. Result of the quantitative partitioning analysis indicates that the dynamic and thermodynamic effects respectively contribute 297.6% and −234% to the reduction of extreme precipitation in AM. The decline in EPCP frequency dominates the decrease in EPEs. In JJA, the increased extreme precipitation after the early 1990s is attributed to both the interdecadal increase in the EPCP frequency and the increasing trend in the atmospheric moisture. The dynamic and thermodynamic changes play almost equal roles in the increased extreme precipitation in JJA.

Increased population exposures to extreme precipitation in Central Asia under 1.5 ℃ and 2 ℃ global warming scenarios

The increase in extreme precipitation (EP) may pose a serious threat to the health and safety of population in arid and semi-arid regions. The current research on the impact of EP on population in Central Asia (CA) is insufficient and there is an urgent need for a comprehensive assessment. Hence, we opted for precipitation and temperature data under two Shared Socioeconomic Pathways (SSP2–4.5 and SSP5–8.5) from ten Global Climate Models (GCMs), which were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). By integrating population data in 2020 and 2050 (SSP2 and SSP5), we investigated the future changes in EP and population exposure in CA under 1.5 °C and 2 °C global warming scenarios (GWSs). Our analysis indicates that EP in CA is projected to increase with global warming. Under the SSP5–8.5, the maximum daily precipitation (Rx1day) exhibits an average response rate to global warming of 3.58 %/K (1.99–4.06 %/K). With rising temperatures, an increasing number of areas and populations in CA will be impacted by EP, especially in the Fergana valley. Approximately 25% of the population (land area) in CA is exposed to Rx1day with increases of more than 8.31% (9.32%) under 1.5 °C GWS and 14.18% (13.25%) under 2 °C GWS. Controlling temperature rise can be effective in reducing population exposures to EP. For instance, limiting the temperature increase to 1.5 °C instead of 2 °C results in a 2.79% (1.75%–4.59%) reduction in population exposure to Rx1day. Finally, we found that climate change serves as the predominant factor influencing the population exposure to EP, while the role of population redistribution, although relatively minor, should not be disregarded. Particularly for prolonged drought, the role of population redistribution manifests negatively.

Predicting extreme sub-hourly precipitation intensification based on temperature shifts

Abstract. Extreme sub-hourly precipitation, typically convective in nature, is capable of triggering natural disasters such as floods and debris flows. A key component of climate change adaptation and resilience is quantifying the likelihood that sub-hourly extreme precipitation will exceed historical levels in future climate scenarios. Despite this, current approaches to estimating future sub-hourly extreme precipitation return levels are deemed insufficient. The reason for this can be attributed to two factors: there is limited availability of data from convection-permitting climate models (capable of simulating sub-hourly precipitation adequately) and the statistical methods we use to extrapolate extreme precipitation return levels do not capture the physics governing global warming. We present a novel physical-based statistical method for estimating the extreme sub-hourly precipitation return levels. The proposed model, named TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX), is based on a parsimonious non-stationary and non-asymptotic theoretical framework that incorporates temperature as a covariate in a physically consistent manner. We first explain the theory and present the TENAX model. Using data from several stations in Switzerland as a case study, we demonstrate the model's ability to reproduce sub-hourly precipitation return levels and some observed properties of extreme precipitation. We then illustrate how the model can be utilized to project changes in extreme sub-hourly precipitation in a future warmer climate only based on climate model projections of temperatures during wet days and on foreseen changes in precipitation frequency. We conclude by discussing the uncertainties associated with the model, its limitations, and its advantages. With the TENAX model, one can project sub-hourly precipitation extremes at different return levels based on daily scale projections from climate models in any location globally where observations of sub-hourly precipitation data and near-surface air temperature are available.

Long‐term evolution and non‐stationary behaviours of daily and subdaily precipitation extremes in China

AbstractThe rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario.

On the response of daily precipitation extremes to local mean temperature in the Yangtze River basin

This study explores the response of daily extreme precipitation (PEX) to local mean temperatures (T) of the previous 0-day (T0), 1-day (T1), and 1 to 9-days (T1–9) in the Yangtze River basin (YRB) using the non-stationarity test, binning method and copula theory. Results show that the annual scaling rate of PEX with T during 1961–2020 exhibits positive, negative and positive changing trends from southwest to northeast with the maximum magnitudes in the west, and experiences significant abrupt change only at 14.1% grids. The PEX-T scaling relation in the entire study period is predominantly characterized by a peak pattern over the YRB, with precipitation peaking at about 8 °C in the western highlands and at 25 °C in the east-central lowlands. However, the use of T1–9 yields a positive PEX-T pattern at more grids compared to T1 and T0, which highlights the important role of atmospheric moisture residence time in minimizing cooling artefacts in the scaling estimation. Generally, the full scaling rate of PEX with T in the study period is larger in the west-central part but smaller in the eastern YRB, and more sensitive to antecedent temperatures, with the ranges of −8–20%/°C, −8–30%/°C and −10–30%/°C against T0, T1 and T1–9, respectively. As precipitation intensifies, the full scaling decreases in plateau-mountain areas but increases in low-lying regions, gradually approaching the Clausius-Clapeyron (CC) scaling and exhibiting a “super-CC, CC-like and sub-CC scaling” pattern from west to east. Additionally, heavier precipitation events are most likely to occur at higher local temperatures in most areas even with peak structure. These findings provide new insights into the PEX-T relationships, which is conducive to understanding future changes in precipitation extremes and assessing precipitation-related risks under climate change.

Spatial and Temporal Variability Characteristics and Driving Factors of Extreme Precipitation in the Wei River Basin

As global climate change intensifies, the global atmospheric circulation process is undergoing significant changes, and the local water vapor pattern has also changed. This study takes the Wei River Basin as the research area. Firstly, an evaluation index system for extreme precipitation was established, and the time-series characteristics of the magnitude, frequency, and duration of extreme precipitation were analyzed. Statistical methods were used to analyze the non-consistency in time-series changes in extreme precipitation indicators. Using spatial heterogeneity analysis methods, the spatial variation differences in extreme precipitation in the Wei River Basin were identified. This study selected the El Niño-Southern Oscillation (ENSO) index, global land-ocean temperature index (LOTI), and land surface temperature (LST) index to quantitatively evaluate the impact of climate change on regional extreme precipitation and analyzed the correlation between temperature and extreme precipitation, identifying the key driving factors of extreme precipitation changes. The conclusions of this study are as follows: (1) The southern region of the Wei River Basin experiences more frequent and intense precipitation events, while the northern region experiences relatively few. (2) From 1981 to 2021, the intensity, frequency, and duration of precipitation events in the Wei River Basin gradually increased, with the most significant increase in extreme precipitation in the Guanzhong Plain. (3) Global climate change has an important impact on precipitation events in the Wei River Basin. The increase in the ENSO, LOTI, and LST indices may indicate an increase in the probability of drought and flood events in the Wei River Basin. The relationships between extreme precipitation and temperature present a peak structure. This conclusion is helpful to better understand the impact of climate change on extreme precipitation in the Wei River Basin and provides some support for the response to extreme meteorological events under the background of future climate change.

Future changes in the precipitation regime over the Arabian Peninsula with special emphasis on UAE: insights from NEX-GDDP CMIP6 model simulations

Global warming can profoundly influence the mean climate over the Arabian Peninsula, which may significantly influence both natural and human systems. The present study aims to investigate the changes in the precipitation regime in response to climate change over the Arabian Peninsula, with special emphasis on the United Arab Emirates (UAE). This work is performed using a sub-set of high-resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models under three different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The changes are analyzed in three phases such as 2021–2050 (near future), 2051–2080 (mid future) and 2080–2100 (far future), with the period of 1985–2014 as the baseline. This study represents the first attempt to utilize data from NEX-GDDP models to project the regional patterns of precipitation regime across the Arabian Peninsula. Results suggest that the annual precipitation is expected to increase over most of the UAE by up to 30%, particularly intense from the mid-future onwards in all scenarios. Specifically, the spatiotemporal distribution of precipitation extremes such as intensity, 1-day highest precipitation, and precipitation exceeding 10 mm days are increasing; in contrast, the consecutive dry days may decrease towards the end of the century. The results show that the changes in extreme precipitation under a warming scenario relative to the historical period indicate progressive wetting across UAE, accompanied by increased heavy precipitation events and reduced dry spell events, particularly under the high emission scenarios. A high-resolution dataset is essential for a better understanding of changes in precipitation patterns, especially in regions where more detailed information is needed on a local scale to achieve water, food security, and environmental sustainability to formulate effective adaptation strategies for mitigating the potential risks and consequences associated with variations in wet and dry conditions.

Changes in climate indicators of degradation of UNESCO cultural heritage sites in the European part of Russia

Cultural heritage sites are under threat of destruction due to the impact of climate change and related hazards such as rainstorms, floods, droughts. High vulnerability to natural disasters and important historical and socio-cultural value require special attention to World Heritage properties. The goal of the work is to assess the impact of climate on the destruction of building materials of UNESCO cultural heritage sites for four cities in the European part of Russia (Derbent, Kazan, Petrozavodsk and Pskov). The work uses daily data on air temperature and precipitation for the period 1961–2020. Five indices were used to assess the impact of frost (days with air temperatures below 0°C and minus 5°C, freeze-thaw cycles, Wet-frost index and frost decay exposure index) and two indices of extreme air temperatures and precipitation (percentage of days with maximum air temperature higher 90th percentile and number of days with precipitation above 99th percentile). In addition, changes in the indices between two climatic periods, 1961–1990 and 1991–2020, were estimated. The results showed a reduction in the risk of damage to cultural heritage sites by low air temperatures, while the frequency of freeze-thaw cycles increased in the northern regions against the backdrop of rising surface air temperatures. The change in extreme temperatures and precipitation has a positive trend, which increases the associated risks of destruction of materials of cultural heritage sites. Differences in climatic conditions require an individual selection of indicators to assess the impact of climate change on the destruction of building materials and an integrated approach for each cultural heritage site.

Constraining the Pattern and Magnitude of Projected Extreme Precipitation Change in a Multimodel Ensemble

Abstract Projections of precipitation extremes over land are crucial for socioeconomic risk assessments, yet model discrepancies limit their application. Here we use a pattern-filtering technique to identify low-frequency changes in individual members of a multimodel ensemble to assess discrepancies across models in the projected pattern and magnitude of change. Specifically, we apply low-frequency component analysis (LFCA) to the intensity and frequency of daily precipitation extremes over land in 21 CMIP-6 models. LFCA brings modest but statistically significant improvements in the agreement between models in the spatial pattern of projected change, particularly in scenarios with weak greenhouse forcing. Moreover, we show that LFCA facilitates a robust identification of the rates at which increasing precipitation extremes scale with global temperature change within individual ensemble members. While these rates approximately match expectations from the Clausius-Clapeyron relation on average across models, individual models exhibit considerable and significant differences. Monte Carlo simulations indicate that these differences contribute to uncertainty in the magnitude of projected change at least as much as differences in the climate sensitivity. Last, we compare these scaling rates with those identified from observational products, demonstrating that virtually all climate models significantly underestimate the rates at which increases in precipitation extremes have scaled with global temperatures historically. Constraining projections with observations therefore amplifies the projected intensification of precipitation extremes as well as reducing the relative error of their distribution.

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.