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This work examines the changes in precipitation characteristics over the Indian region using the latest high‐resolution CORDEX‐CORE model simulations. Individual RCM (COSMO, RegCM4.7, and REMO) experiments, their ensembles, and all RCM ensembles are examined to evaluate their performance using various statistical metrics. It aims to provide a holistic idea for choosing better models for specific analysis (variability, climatology, temporal evolution, indices, etc.) of precipitation distribution over India. The COSMO model experiments show lesser mean bias and have a high correlation coefficient (>0.8) compared to REMO and RegCM4. Each RCM ensemble performs better than its members in representing the spatial patterns of precipitation. Interestingly, the RCM experiments downscaling the forcings from MPI_ESM GCMs and ERA‐Interim outperforms other RCM experiments in the present day period. The probability distribution function reflects that the mean frequency and the intensity of precipitation are best represented in the RegCM4 ensemble. Further, most RCMs agree to a robust increase in low‐intensity rainfall (>0.1–4 mm⋅day‑1) in the future under the RCP8.5 scenario. The intraseasonal variability in terms of active and break spells are best represented in the RegCM4 ensemble. There exists uncertainty in terms of projection of active phases, while all model experiments agree to the decrease in the break phases in the near future compared to the present day. The projected consecutive dry (wet) days and their spells will decrease (increase) over India. The heavy precipitation events are expected to increase (by 18–32 days) along with its contribution (by 14–48%) towards the total precipitation over entire India indicating a possible increase in flooding events in the future. The present‐day ISM characteristics is well captured in the model ensemble compared to individual experiments. The model ensemble indicates towards a robust increase in low‐intensity rainfall in the future. The very heavy precipitation events and its contribution towards total precipitation are increasing in the future. [ABSTRACT FROM AUTHOR]

Precipitation regimes have been predicted to shift to more extreme patterns that are characterized by heavy rainfall events and longer dry intervals. Spatiotemporal variation of precipitation extremes may significantly influence the local ecosystem, especially in the arid and semi‐arid regions. This study investigated the variations in extreme precipitation indices across the arid and semi‐arid land of China from 1960 to 2018. Spatial autocorrelation analysis was also conducted to evaluate spatial autocorrelations within the study area. There was generally an increasing trend of precipitation intensity, however, most stations in the low‐altitude semi‐arid regions showed non‐significant trend. The frequency indices generally showed a decreasing trend in consecutive dry days, and an increasing trend in precipitation days in the arid land and high‐altitude semi‐arid regions, suggesting that the arid and high‐altitude semi‐arid regions are becoming wetter. Signals of decreasing precipitation intensity and frequency were still found, such as increasing trends in consecutive dry days in the stations from the low‐altitude semi‐arid regions, implying the possibility of increased drought risk at small spatial scale. The global Moran's I values indicated that the spatial autocorrelation of the trend in the low‐altitude semi‐arid region is high. The local Moran's I showed that the number of stations with wetting trend in the arid region were much higher than that in the semi‐arid regions. These observations imply that the precipitation extremes in the arid and semi‐arid regions of China are becoming more homogeneous. Hence, this study can serve as a useful reference to the policymakers and stakeholders for framing water resource and ecosystem management strategies in arid regions. [ABSTRACT FROM AUTHOR]

Climate change has intensified the hydrologic cycle globally, increasing the magnitude and frequency of large precipitation events, or deluges. Dryland ecosystems are expected to be particularly responsive to increases in deluge size, as their ecological processes are largely dependent on distinct soil moisture pulses. To better understand how increasing deluge size will affect ecosystem function, we conducted a field experiment in a native semiarid shortgrass steppe (Colorado, USA). We quantified ecological responses to a range of deluge sizes, from moderate to extreme, with the goal of identifying response patterns and thresholds beyond which ecological processes would not increase further (saturate). Using a replicated regression approach, we imposed single deluges that ranged in size from 20 to 120 mm (82.3rd to >99.9th percentile of historical event size) on undisturbed grassland plots. We quantified pre‐ and postdeluge responses in soil moisture, soil respiration, and canopy greenness, as well as leaf water potential, growth, and flowering of the dominant grass species (Bouteloua gracilis). We also measured end of season above‐ and belowground net primary production (ANPP, BNPP). As expected, this water‐limited ecosystem responded strongly to the applied deluges, but surprisingly, most variables increased linearly with deluge size. We found little evidence for response thresholds within the range of deluge sizes imposed, at least during this dry year. Instead, response patterns reflected the linear increase in the duration of elevated soil moisture (2–22 days) with increasing event size. Flowering of B. gracilis and soil respiration responded particularly strongly to deluge size (14‐ and 4‐fold increases, respectively), as did ANPP and BNPP (~60% increase for both). Overall, our results suggest that this semiarid grassland will respond positively and linearly to predicted increases in deluge size, and that event sizes may need to exceed historical magnitudes, or occur during wet years, before responses saturate. [ABSTRACT FROM AUTHOR]

The atmospheric water holding capacity will increase with temperature according to Clausius-Clapeyron scaling and affects precipitation. The rates of change in future precipitation extremes are quantified with changes in surface air temperature. Precipitation extremes in China are determined for the 21st century in six simulations using a regional climate model, RegCM4, and 17 global climate models that participated in CMIP5. First, we assess the performance of the CMIP5 models and RCM runs in their simulation of extreme precipitation for the current period (RF: 1982–2001). The CMIP5 models and RCM results can capture the spatial variations of precipitation extremes, as well as those based on observations: OBS and XPP. Precipitation extremes over four subregions in China are predicted to increase in the mid-future (MF: 2039–58) and far-future (FF: 2079–98) relative to those for the RF period based on both the CMIP5 ensemble mean and RCM ensemble mean. The secular trends in the extremes of the CMIP5 models are predicted to increase from 2008 to 2058, and the RCM results show higher interannual variability relative to that of the CMIP5 models. Then, we quantify the increasing rates of change in precipitation extremes in the MF and FF periods in the subregions of China with the changes in surface air temperature. Finally, based on the water vapor equation, changes in precipitation extremes in China for the MF and FF periods are found to correlate positively with changes in the atmospheric vertical wind multiplied by changes in surface specific humidity (significant at the p < 0.1 level). [ABSTRACT FROM AUTHOR]

Atmospheric moisture holding capacity increases with temperature by about 7% per °C according to the Clausius‐Clapeyron relationship. Thermodynamically then, precipitation intensity should exponentially intensify and thus worsen flood conditions as the climate warms. However, regional and global analyses often report a nonmonotonic (hook) scaling of precipitation and runoff, in which extremes strengthen with rising temperature up to a maximum or peak point (Tpp) and decline thereafter. The underlying cause of this hook structure is not yet well‐understood, and whether it may shift and/or regulate storm runoff extremes under anthropogenic climate warming remains unknown. Here, we examine temperature scaling of precipitation and storm runoff extremes under different climate conditions using observations and large ensemble hydroclimatic simulations over mainland China. In situ observations suggest a spatially homogeneous, negative response of relative humidity to warming climates over 34.6% of the land area, and the remaining hook‐dominated regions usually show a colder Tpp than that of precipitation or storm runoff extremes. The precipitation and streamflow series over mainland China's catchments throughout the 21st century are projected by a model cascade chain under a high‐end emission scenario (RCP 8.5), which involves 31 CMIP5 climate models, 11 CMIP6 climate members, a daily bias correction method, and four lumped conceptual hydrological models. The CMIP5 ensemble projects that the hook structures shift toward warmer temperature bins, resulting in 10%–30% increases in storm runoff extremes over mainland China, while the CMIP6 ensemble projects more severe flood conditions in future warming climates. Plain Language Summary: China is threatened by river floods and has frequently suffered from significant ecosystem damage and economic loss under a changing environment. Storm runoff extremes, an important indicator of flooding regime, have received increasing attention, especially regarding their response to a warming climate. Previous studies have observed a nonmonotonic relationship (called a hook structure) between storm runoff extremes and atmospheric temperatures, in which the extremes strengthen with warming up to a peak point and decline thereafter in a hotter environment. Here, we investigate the ability of such behavior to constrain the intensification of future storm runoff extremes, as well as the underlying mechanisms. We find that the hook structures are not fixed under climate warming but peak at warmer temperatures and higher storm runoff rates. As a result, future flooding risks in mainland China will not be constrained by the hook structures but progressively increase by roughly 10%–30%. Key Points: The temperature scaling of precipitation, storm runoff, and relative humidity are characterized over mainland China's catchmentsPrecipitation and storm runoff usually exhibit a hook structure, with a warmer peak point temperature than that of relative humidityThe hook structures are not fixed under climate change but shift toward warmer temperatures and higher extreme rainfall and runoff rates [ABSTRACT FROM AUTHOR]

Precipitation variability analysis, on different spatial and temporal scales, has been of great concern during the past century because of the attention given to global climate change by the scientific community. According to some recent studies, the Iranian territory has been experienced a precipitation variability, especially in the last 50 years, and the arid and semi-arid areas seem to be more affected. The present study aims to analyze precipitation extreme indices over a wide time interval and a wide area, detecting potential trends and assessing their significance. The investigation is based on a wide range of daily and multi-day precipitation statistics encompassing basic characteristics and heavy precipitation. Two different methods of trend analysis and statistical testing are applied, depending on the nature of the statistics. Linear regression is used for statistics with a continuous value range, and logistic regression is used for statistics with a discrete value range. The trends are calculated on annual and seasonal bases for the years 1951-2007. Statistical analysis of the database highlight that a clear trend signal is found with a high number of sites with a statistically significant trend. In winter, significant increases are found for all statistics related to precipitation strength and occurrence. In spring, statistically, significant increases are found only for the statistics related to heavy precipitation, whereas precipitation frequency and occurrence statistics show little systematic change. The trend signal is strongest in highlands and mountainous terrains. In autumn and summer, the heavy and basic precipitation statistics did not show statistically significant trends. [ABSTRACT FROM AUTHOR]

Understanding precipitation extremes over Monsoon Asia is vital for water resource management and hazard mitigation, but there are many gaps and uncertainties in observations in this region. To better understand observational uncertainties, this study uses a high-resolution validation dataset to assess the consistency of the representation of annual daily precipitation maxima (Rx1day) over land in 13 observational datasets from the Frequent Rainfall Observations on Grids (FROGS) database. The FROGS datasets are grouped into three categories: in situ-based and satellite-based with and without corrections to rain gauges. We also look at three sub-regions: Japan, India, and the Maritime Continent based on their different station density, orography, and coastal complexity. We find broad similarities in spatial and temporal distributions among in situ-based products over Monsoon Asia. Satellite products with correction to rain gauges show better general agreement and less inter-product spread than their uncorrected counterparts. However, this comparison also reveals strong sub-regional differences that can be explained by the quantity and quality of rain gauges. High consistency in spatial and temporal patterns are observed over Japan, which has a dense station network, while large inter-product spread is found over the Maritime Continent and India, which have sparser station density. We also highlight that while corrected satellite products show improvement compared to uncorrected products in regions of high station density (e.g., Japan) they have mixed success over other regions (e.g., India and the Maritime Continent). In addition, the length of record available at each station can also affect the satellite correction over these poorly sampled regions. Results of the additional comparison between all considered datasets and the sub-regional high resolution dataset remain the same, indicating that the overall quality of the station network has implications for the reliability of the in situ-based products derived and also the satellite products that use a correction to in situ data. Given these uncertainties in observations, there is no single best dataset for assessment of Rx1day in Monsoon Asia. In all cases we recommend users understand how each dataset is produced in order to select the most appropriate product to estimate precipitation extremes to fit their purpose.

Abstract Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986–2005) and future periods (2016–2035 and 2046–2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5‐SSP3 scenario produces the highest global population exposure for 2046–2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6‐SSP1 scenario produces the highest global GDP exposure for 2046–2065, with a 5.56‐fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $‐days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64–77% and 78–91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures.

Water managers need non-stationary tools to better characterize precipitation extremes. Statistical approaches based on extreme value theory (EVT) are increasingly being used, but few end-to-end generalized workflows are available. In this paper, a step-by-step framework is demonstrated for developing an EVT model that considers the influence of dominant weather patterns on precipitation extremes in a watershed. Specifically, the Point Process (PP) model is utilized, which is a unified statistical framework for modeling the frequency and magnitude of extremes above a threshold. Because threshold selection can be subjective, a demonstration of how to go about selecting a threshold is provided; in particular, by examining a range of thresholds. The workflow is applied to daily precipitation from the Rio Grande watershed in New Mexico. In this arid watershed, extreme precipitation events substantially contribute to total runoff. An improved understanding of the drivers and extent of changes in extreme precipitation is essential for water resource and risk management. In addition to a stationary PP model without covariates, several covariates are examined for inclusion in the location and scale parameters. The significance of including the covariates is assessed, as well as several additional criteria, including if the covariate(s) make intuitive sense and if it is a good candidate for statistical downscaling (i.e., methods that relate large-scale variables to the local scale). A final PP model is selected that includes the wet weather types in the location and scale parameters. This model is applied in a downscaling context using a large ensemble of climate projections, which shows that the frequency of exceeding a high threshold increases after 2050, but the conditional likelihood of exceeding the maximum observed precipitation stays relatively constant.

Abstract Changes in precipitation patterns as a result of increased atmospheric water vapour in a warming environment over the Indian region have been reported in the past few decades. However, these studies have not focused much on exploring the changes in atmospheric water vapour in a changing climate. The present study focuses on examining the variability of integrated precipitable water (IPW) over the Indian region derived from the Modern Era Retrospective‐Analysis for Research and Application (MERRA 2) model in a warming environment. The results suggest that the IPW has increased significantly in recent decades. Changes in the IPW are consistent with changes in regional warming over India. The results also point out an increase of about 4.98% ± 1.26% in the IPW for a 1°C increase in regional warming. The variability of IPW has been examined in a warming climate. An increase in regional temperature shows coherent variation with IPW. A decadal increase of about 1.69% has been reported in the IPW.

The coastal regions of South Ecuador and Peru belong to the areas experiencing the strongest impact of the El Niño Southern Oscillation phenomenon. However, the impact and dynamic development of weather patterns during those events are not well understood, due to the sparse observational networks. In spite of neutral to cold conditions after the decaying 2015/16 El Niño in the central Pacific, the coastal region was hit by torrential rainfall in 2017 causing floods, erosion and landslides with many fatalities and significant damages to infrastructure. A new network of X-band weather radar systems in South Ecuador and North Peru allowed, for the first time, the spatio-temporally high-resolution monitoring of rainfall dynamics, also covering the 2017 event. Here, we compare this episode to the period 2014–2018 to point out the specific atmospheric process dynamics of this event. We found that isolated warming of the Niño 1 and 2 region sea surface temperature was the initial driver of the strong rainfall, but local weather patterns were modified by topography interacting with the synoptic situation. The high resolution radar data, for the first time, allowed to monitor previously unknown local spots of heavy rainfall during ENSO-related extreme events, associated with dynamic flow convergence initiated by low-level thermal breezes. Altogether, the coastal El Niño of 2017, at the same time, caused positive rainfall anomalies in the coastal plain and on the eastern slopes of the Andes, the latter normally associated only with La Niña events. Thus, the 2017 event must be attributed to the La Niña Modoki type.

This paper examines new evidence from observational and detection and attribution studies of changes in extreme precipitation in China since the early 1960s. We have also designed a series of sensitivity tests to explore the robustness of detection and attribution results to the differences in sample size, in extreme precipitation index, and in data processing procedure. Our analyses used the most recent update of observational records as well as simulations conducted with the climate models participated in the Coupled Model Intercomparison Project Phase 6. Based on the existing studies and our additional analyses, we found that human influence is detectable in extreme precipitation in China regardless of the period, extreme precipitation index, or data treatment considered, in both China as a whole and in northern and southern China separately. We also found, as is often encountered in detection and attribution studies, it is difficult to separate the contribution from anthropogenic forcing from that of natural external forcing, and it is also challenging to decompose the anthropogenic component into a greenhouse gas forcing component and a component that reflects other anthropogenic forcing agents (dominantly, aerosols).

Using daily precipitation data from 344 meteorological stations, the temporal and spatial variations and trends of precipitation extremes were analyzed, and the frequency distribution of extreme precipitation and the differences between urban and rural stations were also investigated over East China during 1961–2015. Ten extreme precipitation indices from R‑language Climate Index (RClimDex) were selected and the change characteristics were examined using the methods of trend analysis, Mann-Kendall (M‑K) test, and spatial analysis module of ArcGIS 10.2.1. Results indicated that almost all of the regional precipitation indices related to the frequency or intensity of precipitation extremes had increasing trends in the past 55 years, with the indices of Rx1day, Rx5day, R95p, R99P, SDII and R25mm increasing significantly. Spatially, the distribution of precipitation extremes had obvious regional differences. The increase of PRCPTOT, SDII, R95p, R20mm and R25mm were observed mainly in the southern and middle parts of East China, though most of the trends were statistically insignificant. For CWD, a decreasing trend was dominant over almost all study area. There were also prominent differences of precipitation extremes between urban and suburban stations. Urbanization had generally accelerated the increasing rate for heavy precipitation days, extreme precipitation amount and precipitation intensity in East China. More detailed studies are needed to fully understand the changes in extreme precipitation and its relationship with other factors, including urbanization.

Abstract The impacts of air‐sea interactions on the representation of tropical precipitation extremes are investigated using an atmosphere‐ocean‐mixed‐layer coupled model. The coupled model is compared to two atmosphere‐only simulations driven by the coupled‐model sea‐surface temperatures (SSTs): one with 31 day running means (31 d), the other with a repeating mean annual cycle. This allows separation of the effects of interannual SST variability from those of coupled feedbacks on shorter timescales. Crucially, all simulations have a consistent mean state with very small SST biases against present‐day climatology. 31d overestimates the frequency, intensity, and persistence of extreme tropical precipitation relative to the coupled model, likely due to excessive SST‐forced precipitation variability. This implies that atmosphere‐only attribution and time‐slice experiments may overestimate the strength and duration of precipitation extremes. In the coupled model, air‐sea feedbacks damp extreme precipitation, through negative local thermodynamic feedbacks between convection, surface fluxes, and SST.

Climate extremes have more direct and significant impact than average state on social andecological systems. In this study temporal and spatial distribution of extreme precipitationevents were analyzed based on the daily precipitation data of seven meteorological stationsin the Lake Urmia Basin in Iran from 1987 to 2014. Eleven indices of precipitationextremes were selected and calculated using the RClimDex software. The Mann-Kendalltest was employed to assess trend in extreme precipitation indices, and the Sen's Estimatortest was used to determine the slope of the significant trends. Results demonstrate thatconsecutive dry days (CDD) exhibit an increasing trend. The other indices of precipitationextremes, Consecutive wet days (CWD), heavy precipitation days (R10mm), heavierprecipitation days (R20mm), heaviest precipitation days (R25mm), maximum 1-dayprecipitation (RX1day), maximum 5-day precipitation (RX5day), very wet dayprecipitation (R95), extremely wet day precipitation (R99), simple daily intensity index(SDII), and wet-day precipitation (PRCPTOT) show decreasing trend and time of change inmost indices starting from 1995-1996. However, all the linear trends for each index are notstatistically significant. Decreasing trends in precipitation days were greater than those inprecipitation indices. The slope of trends in extreme precipitation indices showed that thehighest slope of the decreasing trend occurred in wet-day precipitation, consecutive drydays and very wet day precipitation. Spatial distribution for precipitation extremesexhibited a declining trend in most regions in the Lake Urmia Basin. Furthermore, theextreme precipitation indexes had positive correlations with the annual total precipitation,and their correlation coefficients were statistically significant at the 1% significance level,except for consecutive dry days.

Climate change is intensifying the hydrologic cycle globally, increasing both the size and frequency of extreme precipitation events, or deluges. Arid and semi‐arid ecosystems are expected to be particularly responsive to this change because their ecological processes are largely driven by distinct soil moisture pulses. However, since soil moisture, air temperature and plant phenology vary throughout the growing season, deluges will likely have differing impacts on these systems depending on when they occur.We conducted a field experiment to assess how the seasonal timing (early, middle or late growing season) of a single deluge (70‐mm precipitation event) altered key ecological processes in the semi‐arid shortgrass steppe of North America.Regardless of timing, a single deluge stimulated most ecosystem processes, but a deluge at mid‐season caused the greatest increase in soil respiration, canopy greenness, above‐ground net primary production (ANPP) and growth and flowering of the dominant plant species Bouteloua gracilis. In contrast, below‐ground net primary production (BNPP) was insensitive to deluge timing, with a consistent BNPP increase in all the deluge treatments that was almost twice as large as the ANPP response. This BNPP response was largely driven by enhanced root production at the 10–20 cm, rather than 0–10 cm, soil depth.Synthesis. In a semi‐arid ecosystem, a single deluge can have season‐long impacts on many ecosystem processes, but responses can be mediated by event timing. Therefore, predicting responses of semi‐arid ecosystems to more dynamic precipitation regimes, and subsequent impacts on the global carbon budget, will require knowledge of how deluge magnitude, frequency and timing are being altered by climate change. [ABSTRACT FROM AUTHOR]

The impacts of anthropogenic land use and land cover (LULC) changes on the spatiotemporal distribution of precipitation in megacities have been highlighted in studies on urban climate change. In this study, we conducted a quantitative analysis of urban growth on the impact on precipitation in Shanghai, China. We considered four periods of LULC data in 1979, 1990, 2000 and 2010, in addition to the long-term (1979–2010) trend of daily precipitation. The results indicate that the trend in precipitation exhibit different characteristics for urban (Ur), outskirt of urban (OUr) and outer suburb (OS) regions. Most Ur regions had an upward trend in annual and extreme precipitation during 1979–2010, while annual precipitation for the OUr and OS regions exhibited a decreasing trend. From 1979 to 2010, the areas of fastest expansion were located in the OUr region. The OS region, far away from the central area, had a relatively lower rate of change. In addition, OUr regions with rapid LULC changes exhibited higher increasing trends in annual and daily extreme precipitation, which is critical for the identification of frequent precipitation areas and the reliable projection of further changes. [ABSTRACT FROM AUTHOR]

Despite increasing evidence of intensification of extreme precipitation events associated with a warming climate, the magnitude of peak river flows is decreasing in many parts of the world. To better understand the range of relationships between precipitation extremes and floods, we analyzed annual precipitation extremes and flood events over the contiguous United States from 1980 to 2014. A low correlation (less than 0.2) between changes in precipitation extremes and changes in floods was found, attributable to a small fraction of co‐occurrence. The covariation between precipitation extremes and floods is also substantially low, with a majority of catchments having a coefficient of determination of less than 0.5, even among the catchments with a relatively high fraction of annual maxima precipitation that can be linked to floods. The findings indicate a need for more investigations into causal mechanisms driving a nonlinear response of floods to intensified precipitation extremes in a warming climate. Key Points: We assess co‐occurrence and covariation between precipitation extremes and floods to explore the range of their relationshipsThe spatial pattern of changes in precipitation extremes explains less than 20% of the spatial pattern of changes in floodsMost catchments have a covariation of less than 0.5 between annual precipitation extremes and annual floods [ABSTRACT FROM AUTHOR]

At regional to local scales internal variability is expected to be a dominant source of uncertainty in analyzing precipitation extremes and mean precipitation even far into the 21st century. A debated topic is whether a faster increase in subdaily precipitation extremes can be expected. Here we analyzed seasonal maximum precipitation in various time steps (3 hr, days, and 5 days) from a high‐resolution 50‐member large‐ensemble (CRCM5‐LE) and compared them to changes in mean precipitation over Europe. Our results show that the magnitude of change in extreme precipitation varies for season and duration. Subdaily extremes increase at higher rates than daily extremes and show higher scaling with temperature. Northern Europe shows widespread scaling above Clausius‐Clapeyron of subdaily extremes in all seasons and for daily extremes in winter/spring. Scaling above Clausius‐Clapeyron is also visible over Eastern Europe in winter/spring. For most regions and seasons the forced response emerges from the internal variability by midcentury. Plain Language Summary: The knowledge on how and why the intensity and frequency of extremes changes is critical to a resilient society. Our adaptive measures that are currently in place are based on observed extremes of the past. We know that observations are only one realization of a chaotic system and that the climate system is altered by natural variations, and anthropogenic contributions. Since we can only measure one realization of the world, we need climate models to investigate the influence of natural variability and anthropogenic factors. In this study we focus on the contribution of natural variability and the detection of regional patterns of changes in extreme precipitation. We used regional climate simulations, driven by multiple runs of global climate simulations under the same emission scenario, but with slight changes at the start of the simulation to imitate the butterfly effect of the climate system and simulate natural variability. We have found that natural variability plays a dominant role in the first half of the 21st century. But we have also found that subdaily extreme precipitation is increasing at a higher rate than daily extremes and that some of this change can be attributed to the warming of the atmosphere. Key Points: Subdaily extreme precipitation intensifies faster than daily extremes and mean precipitationOn regional scales internal variability remains a dominant source of uncertainty until the end of the 21st centuryWidespread scaling rates of subdaily extremes above Clausius‐Clapeyron over Northern and Eastern Europe [ABSTRACT FROM AUTHOR]

Extreme precipitation indices (EPIs) were defined to quantify the precipitation extremes in Singapore, a typical tropical country situated near the equator. The paper investigated the spatial and temporal variability of precipitation extremes based on seventeen EPIs using non‐parametric Mann‐Kendall test and Sen's slope, and further explored the linear and nonlinear relationships between precipitation extremes and four large‐scale global climate oscillations using correlation and wavelet analysis, during the period of 1980–2018 in Singapore. The results indicated that the trends of precipitation extremes varied for different EPIs, regions and stations. Increasing trends dominated thirteen out of seventeen EPIs. The trends of EPIs were scattered and irregularly distributed. The cross‐correlation analysis between different EPIs demonstrated that annual total precipitation on wet days (PRCPTOT) was strongly correlated with other EPIs. The result of composite analysis indicated that El Niño Southern Oscillation (ENSO) exerted stronger impacts on southwest monsoon season (SMS) precipitation than PRCPTOT and northeast monsoon season (NMS) precipitation. The SMS precipitation composite suggested that ENSO created more influence on dry spells than wet spells. The linear and nonlinear relationships revealed that all climate oscillations were negatively correlated with precipitation. The wavelet coherence and phase differences were consistent with the results of correlation analysis, indicating possible prediction of precipitation extremes using climate oscillations as potential predictors. [ABSTRACT FROM AUTHOR]

Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986–2005) and future periods (2016–2035 and 2046–2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5‐SSP3 scenario produces the highest global population exposure for 2046–2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6‐SSP1 scenario produces the highest global GDP exposure for 2046–2065, with a 5.56‐fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $‐days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64–77% and 78–91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures. Plain Language Summary: The risk of precipitation extremes is likely to increase with climate change. Socioeconomic exposure is the key component for assessing the risk of such events. The projections of five global climate models (GCMs), forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in shared socioeconomic pathways (SSPs), were used to quantify socioeconomic exposure to precipitation extremes for a base period (1986–2005) and future periods (2016–2035 and 2046–2065). The exposure of the global population for 2046–2065 is highest under the RCP8.5‐SSP3 scenario, and the global GDP exposure for 2046–2065 is highest under the RCP2.6‐SSP1 scenario. Socioeconomic effects (population and GDP effects) play the main roles in the changes in exposure at both global and continental scales. Asia and Africa should be given more attention due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries should also commit to effective adaptation measures. Key Points: Global population and GDP exposures are highest for 2046–2065 in RCP8.5‐SSP3 and RCP2.6‐SSP1, 8.46 × 1010 person‐days and 2.29 × 1015PPP $‐daysIncreases in population and GDP are the dominant contributors, accounting for over 60% of future changes in socioeconomic exposureEffective adaptation is urgently needed for some European countries, for example, Luxembourg, the Netherlands, and Belgium, and in Asia and Africa [ABSTRACT FROM AUTHOR]

Mesoscale convection generates the majority of extreme precipitation in tropical regions. Changes to these precipitation intensities, P, with long‐term modes of climate variability have been hard to assess because they are not well represented in current climate models. Here we stratify a satellite climatology of convective systems by El Niño phase and cloud top temperature. We find that gains (losses) in high precipitation intensity (P˙> 10 mm hr−1) are largest for the deepest (least deep) systems during El Niño relative to La Niña. The surface temperature and wind changes that define El Niño manifest as surface flux changes but are not sufficient to explain these P˙ trends. We explore also the dynamical component of precipitation generation with a vertical momentum budget. Midtropospheric drying in the vicinity of the deepest systems boosts instability and ascent rates during El Niño, while the strengthened large‐scale ascent minimizes the drag force on their updrafts. Plain Language Summary: In the tropics, the majority of high‐intensity precipitation comes from the organization of multiple thunderstorms into a convective system. These systems are not yet well represented in global models, and studies of how their precipitation changes with long‐term modes of climate variability like the El Niño–Southern Oscillation have been limited. Using long‐term satellite data, we find here that the most intense precipitation becomes two times more probable for the deepest systems and 20% less probable for the least deep systems during El Niño. With a budget for the ascent rate within these systems, we illustrate how favorable moisture structure acts as a buoyancy source in the first case and unfavorable circulation acts as a buoyancy sink in the latter case. Key Points: For the deepest convective systems, precipitation rates >10 mm hr−1 become two times more likely during El NiñoThe distribution of net gross moist stability shifts to lower values for these deepest systems during El NiñoDecreased net gross moist stability can be explained by strengthened large‐scale ascent and free tropospheric drying [ABSTRACT FROM AUTHOR]

Finer grids in global climate models could lead to an improvement in the simulation of precipitation extremes. We assess the influence on model performance of increasing spatial resolution by evaluating pairs of high‐ and low‐resolution forced atmospheric simulations from six global climate models (generally the latest CMIP6 version) on a common 1° × 1° grid. The differences in tuning between the lower and higher resolution versions are as limited as possible, which allows the influence of higher resolution to be assessed exclusively. We focus on the 1985–2014 climatology of annual extremes of daily precipitation over global land, and models are compared to observations from different sources (i.e., in situ‐based and satellite‐based) to enable consideration of observational uncertainty. Finally, we address regional features of model performance based on four indices characterizing different aspects of precipitation extremes. Our analysis highlights good agreement between models that precipitation extremes are more intense at higher resolution. We find that the spread among observations is substantial and can be as large as intermodel differences, which makes the quantitative evaluation of model performance difficult. However, consistently across the four precipitation extremes indices that we investigate, models often show lower skill at higher resolution compared to their corresponding lower resolution version. Our findings suggest that increasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes, and other improvements (e.g., physics and tuning) may be required. Key Points: Models generally agree on an intensification of precipitation extremes at higher spatial atmospheric resolutionObservational uncertainties are substantial for precipitation extremes, which makes the evaluation of the models difficultIncreasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes [ABSTRACT FROM AUTHOR]

Deep convection is a key process in the climate system and the main source of precipitation in the tropics, subtropics, and mid-latitudes during summer. Furthermore, it is related to high impact weather causing floods, hail, tornadoes, landslides, and other hazards. State-of-the-art climate models have to parameterize deep convection due to their coarse grid spacing. These parameterizations are a major source of uncertainty and long-standing model biases. We present a North American scale convection-permitting climate simulation that is able to explicitly simulate deep convection due to its 4-km grid spacing. We apply a feature-tracking algorithm to detect hourly precipitation from Mesoscale Convective Systems (MCSs) in the model and compare it with radar-based precipitation estimates east of the US Continental Divide. The simulation is able to capture the main characteristics of the observed MCSs such as their size, precipitation rate, propagation speed, and lifetime within observational uncertainties. In particular, the model is able to produce realistically propagating MCSs, which was a long-standing challenge in climate modeling. However, the MCS frequency is significantly underestimated in the central US during late summer. We discuss the origin of this frequency biases and suggest strategies for model improvements. [ABSTRACT FROM AUTHOR]

Convective organization has the potential to impact the strength of precipitation extremes, but numerical models disagree about this influence. This study uses satellite observations to investigate the link between the mesoscale organization of deep convection and precipitation extremes in the Tropics. Extremes in domain‐averaged precipitation are found mostly over the western Pacific and Indian Ocean warm pools, and they primarily depend on the number of deep convective entities within the domain. On the other hand, extremes in local precipitation are found primarily over land, and they increase with the degree of convective organization. Therefore, this observational study shows evidence for a modulation of the strength of tropical precipitation extremes by the spatial organization of deep convection, especially over land. Plain Language Summary: Events of extreme precipitation represent huge threats to ecosystems and society. It is therefore important to understand the conditions that might promote their occurrence. Recent modeling studies have suggested that extreme precipitation events could depend on the spatial organization of deep convection, but there is no consensus among the models. In this study, we use satellite observations to investigate this issue, and we show strong evidence that the occurrence of extreme precipitation over tropical land depends on the degree of mesoscale organization of deep convection. Key Points: The link between tropical precipitation extremes and the mesoscale organization of deep convection is investigated using satellite dataThe strength of local precipitation extremes exhibits a strong dependence on convective organizationLocal precipitation extremes primarily result from changes in the fractional area of heavy precipitation [ABSTRACT FROM AUTHOR]

The atmospheric moisture transport processes are of great importance to the occurrence and intensity of precipitation extremes. In this paper, we review the linkage between processes, including the large‐scale atmospheric circulation, atmospheric moisture transport, and extreme precipitation events. We first summarize the thermodynamic and dynamic processes and moisture transport trackings for historical precipitation extremes. We then focus on the contribution of three major atmospheric moisture transport pathways, that is, atmospheric rivers, low‐level jets, and tropical cyclones, to the occurrence and intensity of regional precipitation extremes. Studies on large‐scale atmospheric circulation driving water vapor transport for precipitation extremes over East Asia and North America were specifically reviewed for the understanding of physical mechanisms and predictability of moisture transport and extreme precipitation events. We then pay more attention to the effects of global warming on atmospheric moisture transport, and thus regional precipitation extremes from the perspectives of thermodynamic and dynamic changes of the atmosphere. In the end, we summarize future research challenges on the physical mechanisms of atmospheric moisture transport that are associated with regional precipitation extremes, especially under a warming climate. This article is categorized under: Science of Water > Water ExtremesScience of Water > Hydrological Processes [ABSTRACT FROM AUTHOR]

Ongoing intensification of the hydrological cycle is altering rainfall regimes by increasing the frequency of extreme wet and dry years and the size of individual rainfall events. Despite long‐standing recognition of the importance of precipitation amount and variability for most terrestrial ecosystem processes, we lack understanding of their interactive effects on ecosystem functioning. We quantified this interaction in native grassland by experimentally eliminating temporal variability in growing season rainfall over a wide range of precipitation amounts, from extreme wet to dry conditions. We contrasted the rain use efficiency (RUE) of above‐ground net primary productivity (ANPP) under conditions of experimentally reduced versus naturally high rainfall variability using a 32‐year precipitation–ANPP dataset from the same site as our experiment. We found that increased growing season rainfall variability can reduce RUE and thus ecosystem functioning by as much as 42% during dry years, but that such impacts weaken as years become wetter. During low precipitation years, RUE is lowest when rainfall event sizes are relatively large, and when a larger proportion of total rainfall is derived from large events. Thus, a shift towards precipitation regimes dominated by fewer but larger rainfall events, already documented over much of the globe, can be expected to reduce the functioning of mesic ecosystems primarily during drought, when ecosystem processes are already compromised by low water availability.Climate change is expected to increase the variability of precipitation, with uncertain consequences for ecosystem functioning. We experimentally removed variability in growing season rainfall patterns across a large gradient of total precipitation amounts in native grassland. On comparing levels of primary productivity with the removal of rainfall variability to productivity under naturally high rainfall variability using long‐term observational data, we discover that rainfall variability reduces ecosystem functioning primarily during dry conditions, and that the mechanism underpinning this is the presence of large rainfall events. [ABSTRACT FROM AUTHOR]

Temperature and precipitation extremes are the dominant causes of natural disasters. In this study, seven indices of extreme temperature and precipitation events in Gansu Province, China, were analysed for the period 1961–2017. An abrupt climate shift was recorded during 1980–1981. Thus, the study period was divided into a preshift (before the climate shift) period 1961–1980 and an aftshift (after the climate shift) period 1981–2017. Comparison of mean extreme indices for preshift and aftshift periods was performed for the purpose of exploring possible increasing/decreasing patterns. Generalized extreme value (GEV) distribution was applied spatially to fit the extreme indices with return periods up to 100 years for preshift/aftshift periods. Singular value decomposition (SVD) was adopted to investigate possible correlation between the extreme climate events and indices of large-scale atmospheric circulation. The results indicate that changes in mean and return levels between the preshift and aftshift periods vary significantly in time and space for different extreme indices. Increase in extreme temperature regarding magnitude and frequency for the aftshift period as compared with the preshift period suggests a change to a warmer and more extreme climate during recent years. Changes in precipitation extremes were different in southern and northern parts of Gansu. The precipitation extremes in the north have increased that can result in more serious floods and droughts in the future. SVD analyses revealed a complex pattern of correlation between climate extremes and indices of large-scale atmospheric circulation. Strengthening of westerlies and weakening of the south summer monsoon contribute to the complex changing patterns of precipitation extremes. Results in this study will contribute to disaster risk prevention and better water management in this area. [ABSTRACT FROM AUTHOR]

Insect declines are being reported worldwide for flying, ground, and aquatic lineages. Most reports come from western and northern Europe, where the insect fauna is well-studied and there are considerable demographic data for many taxonomically disparate lineages. Additional cases of faunal losses have been noted from Asia, North America, the Arctic, the Neotropics, and elsewhere. While this review addresses both species loss and population declines, its emphasis is on the latter. Declines of abundant species can be especially worrisome, given that they anchor trophic interactions and shoulder many of the essential ecosystem services of their respective communities. A review of the factors believed to be responsible for observed collapses and those perceived to be especially threatening to insects form the core of this treatment. In addition to widely recognized threats to insect biodiversity, e.g., habitat destruction, agricultural intensification (including pesticide use), climate change, and invasive species, this assessment highlights a few less commonly considered factors such as atmospheric nitrification from the burning of fossil fuels and the effects of droughts and changing precipitation patterns. Because the geographic extent and magnitude of insect declines are largely unknown, there is an urgent need for monitoring efforts, especially across ecological gradients, which will help to identify important causal factors in declines. This review also considers the status of vertebrate insectivores, reporting bias, challenges inherent in collecting and interpreting insect demographic data, and cases of increasing insect abundance. [ABSTRACT FROM AUTHOR]

Changes in precipitation patterns as a result of increased atmospheric water vapour in a warming environment over the Indian region have been reported in the past few decades. However, these studies have not focused much on exploring the changes in atmospheric water vapour in a changing climate. The present study focuses on examining the variability of integrated precipitable water (IPW) over the Indian region derived from the Modern Era Retrospective‐Analysis for Research and Application (MERRA 2) model in a warming environment. The results suggest that the IPW has increased significantly in recent decades. Changes in the IPW are consistent with changes in regional warming over India. The results also point out an increase of about 4.98% ± 1.26% in the IPW for a 1°C increase in regional warming. The variability of IPW has been examined in a warming climate. An increase in regional temperature shows coherent variation with IPW. A decadal increase of about 1.69% has been reported in the IPW. [ABSTRACT FROM AUTHOR]

The precipitation in Central-East Brazil (CEB) from December to February is heavily influenced by the South Atlantic Convergence Zone (SACZ). The SACZ not only causes considerable rainfall but also has an impact on the underlying ocean. This study examines the extreme precipitation events in CEB and their relationship with the SACZ and sea surface temperature (SST). Empirical Orthogonal Function (EOF) analyses of daily precipitation and vertical velocity at 500 hPa data diagnose the extremes. The grouped events of similar positioning and intensity resulted in 170 extremely wet and 172 dry events. Results indicate that the variability of the SACZ is responsible for extremely wet precipitation events in CEB. Composites of precipitation, SST, and wind anomalies at 850-hPa and 200-hPa characterize their occurrence and resemble SACZ high-intensity variability. Conversely, extremely dry CEB conditions are associated with SACZ southern events (51 events) and SACZ inactivity (121 events). The latter refers to major drought events when upper-level cyclonic circulation favors dry air descending and inhibiting convection over CEB. SACZ southern events have similar atmospheric dynamical patterns as SACZ events but are displaced to the south. The meridional displacement of the South Atlantic Low-Level Jet (SALLJ) and its confluence with the northeasterly flow of the South Atlantic Subtropical High (SASH) are identified as the causes of the cooling or heating of the underlying ocean. The intensity of the extreme event is related to the strength of lower-level wind circulation, while upper-level wind circulation anomalies favor the lower-level effects. The persistence of the systems is related to the development of SST anomalies. • The lower-level wind structure originates the impacts over the underlying ocean during SACZ extreme episodes. • Drought conditions over Central-East Brazil are found during SACZ inactivity and SACZ southern displaced events. • The Southwestern Atlantic Ocean is a possible modulator of the persistence of extreme precipitation events over Central-East Brazil. [ABSTRACT FROM AUTHOR]

Seasonal regional features of the daily precipitation extremes were studied based on Russian meteorological stations datasets for the period of 1991- 2013 compared to the 1961-1990 climate baseline conditions. Precipitation extreme changes were assessed for the most vulnerable regions of Russia with high population density, where precipitation extremes result in negative impacts on the environment and human activities. It was found that the frequency of precipitation extremes in winter and in spring for the period 1991-2013 significantly increased, by 20-40% at the average, in most parts of the case study area. Due to positive trends in daily precipitation extremes changes which was revealed for the winter and spring periods (not exceeding on average 0.2 mm/day/decade), the risks of catastrophic spring floods have been analysed, especially in the areas with a higher recurrence rate of dangerous floods, i.e. - the South Urals region and the Altai region. Strong positive trends of extreme precipitation changes were observed in the Russian Far East region. It indicates higher risk of summer rain floods in the Amur River basin. A significant impact on human activities and in particular population health is associated with revealed trends in hydrological cycle changes that are not relevant to typical meteorological and hydrological regimes. The significant increase of the frequency of extreme summer precipitation events in the Central Chernozem region of European Russia in the period of 1961-2013 was accompanied by the leptospirosis disease incidences.

Recently, a new high-resolution daily downscaled data-set derived from 21 CMIP5 model simulations has been released by NASA, called ‘NASA Earth Exchange Global Daily Downscaled Projections’ (NEX-GDDP). In this study, the performance of this data-set in simulating precipitation extremes and long-term climate changes across China are evaluated and compared with CMIP5 GCMs. The results indicate that NEX-GDDP can successfully reproduce the spatial patterns of precipitation extremes over China, showing results that are much closer to observations than the GCMs, with increased Pearson correlation coefficients and decreased model relative error for most models. Furthermore, NEX-GDDP shows that precipitation extremes are projected to occur more frequently, with increased intensity, across China in the future. Especially at regional to local scales, more information for the projection of future changes in precipitation extremes can be obtained from this high-resolution data-set. Most importantly, the uncertainties of these projections at the regional scale present significant decreases compared with the GCMs, making the projections by NEX-GDDP much more reliable. Therefore, the authors believe that this high-resolution data-set will be popular and widely used in the future, particularly for climate change impact studies in areas where a finer scale is required.

A notable increase in heavy precipitation has been observed over the Netherlands in recent decades. The aim of this study was to assess the influences of urban land use on these extreme precipitation patterns. Significant differences between an earlier multi-decadal period and a recent period were found in the Netherlands between 1961 and 2014. The significant changes in different indices indicate that severe precipitation events were not distributed homogeneously across the study area. The precipitation probability and distribution were assessed using the block maxima approach by comparing observations from urban and rural areas at different timescales. The possible effects of land use on extreme precipitation were assessed by quantifying the differences between urban and rural rain gauge stations according to the spatial gridding method. This study shows that urban land use may have affected the extreme precipitation patterns across the Netherlands. The data from all the categorized stations show that urban areas receive more intense extreme precipitation than do rural areas. Relative to other areas in the Netherlands, the urban areas in the western populated regions of the country exhibit prominent urban land use influences on the extreme precipitation patterns.

The study investigates trends in extreme daily precipitation indices in Bosnia and Herzegovina. A set of daily precipitation time series from four meteorological stations was used for the computation of 13 indices (mostly recommended by the ETCCDI). Trends were calculated for the 1961-2015 periods using RClimDex (1.0) software package. Given the results, a general increase in the extreme precipitation indices is present over the territory of Bosnia and Herzegovina. Upward trends in heavy precipitation indices such as RX1day, RX5day, SDII, R10mm, R20mm, R95p, R95p%, R99p and R99p% indicate changes toward more intense precipitation in Bosnia and Herzegovina. However, most of the estimated trends are not statistically significant. Registered patterns of change were not spatially and temporally coherent and one of the characteristics was that trend signs were mixed. The analyzed precipitation indices trends are directly linked to changes in the dominant large-scale circulation patterns over Northern Hemisphere. Statistically significant negative correlation with NAO, EAWR and AO is found for majority of extreme precipitation indices, particularly strong during winter. Due to their large impact on natural and socio-economic systems, further research on extreme precipitation events is certainly necessary.

Spatiotemporal changes of temperature and precipitation extremes from 1956 to 2015 were analyzed at 200 representative weather stations evenly distributed in the temperate continental zone (TCZ), temperate monsoon zone (TMZ), mountain plateau zone (MPZ), and (sub) tropical monsoon zone (SMZ) of China, using 16 extreme temperature and 11 extreme precipitation indexes. The results showed that warm days (TX90p) and warm nights (TN90p) increased significantly, while cool days (TX10p) and cool nights (TN10p) decreased significantly in the whole China. Overall increasing trends were found for maximum and minimum daily maximum temperature (TXx and TXn) and maximum and minimum daily minimum temperature (TNx and TNn). Warm indexes, including summer days (SU25), tropical nights (TR20), warm spell duration indicator (WSDI), and growing season length (GSL), showed increasing trends, whereas cold indexes such as frost days (FD0), ice days (ID0), cold spell duration indicator (CSDI), and diurnal temperature range (DTR) showed decreasing trends. These extreme temperature indexes exhibited high correlations with mean air temperature. MPZ exhibited the most remarkable change magnitudes among the four zones, while the smallest changes occurred in SMZ. An accelerating warming trend was particularly observed since 1986. Nationally, only daily rainfall intensity (SDII) showed significantly increasing trends, while the increasing trends of other precipitation indexes were not significant. Apart from consecutive wet days (CWD), changes of precipitation extremes presented increasing trends. PRCPTOT and R10mm exhibited the highest correlation coefficient across contrasting climatic zones. Regionally averaged precipitation totals were decreasing in TMZ during 1956–1985, but increasing trends were identified after 1985. The upward tendency of precipitation totals in MPZ and the west part of TCZ may alleviate the pressure of water shortage in arid and semi-arid regions of China, but the upward trend in SMZ, especially in the coastal areas of southeastern China, may aggravate the risk of flood-induced disasters in these regions. [ABSTRACT FROM AUTHOR]

Projections of seasonal extreme precipitation changes in eight Mediterranean subregions between the end of the twentieth and the end of the twenty-first century are analyzed using weighted multi-model ensembles. Weights are based on the performance of predictor variables in the scope of statistical downscaling. Two indices of precipitation scarcity as well as two indices of heavy precipitation are downscaled from global climate model data of the Coupled Model Intercomparison Project phase 3 and 5 (CMIP3, CMIP5) multi-model ensembles, considering two emission scenarios each. Based on the performance with regard to observations of extreme precipitation as well as inter-model consistency, three weighting metrics are calculated and subsequently applied to each ensemble. While meteorological droughts are projected to increase in most cases, the tendency is less pronounced for heavy precipitation events and mostly points towards reduction. The weighting does not affect the multi-model mean changes, but induces a decrease of ensemble spread (although mostly not significant), implying a decrease of model uncertainty. As the ensemble and scenario considered have minor effect on the findings and also the differences between seasons and subregions are not marked, there is strong evidence for enhanced droughts in the Mediterranean region, implying major socio-economic and ecological consequences. [ABSTRACT FROM AUTHOR]

Autumn rainfall in West China, the last rainy season of China, exerts profound impacts on the economic society, therefore, researches on its changes in the context of a warmer world are crucial for better adaptation to climate change. Using the dynamical downscaling performed by the regional climate model RegCM4 from four global climate models, this study firstly evaluated the fidelity of the RegCM4 simulations on the mean and extreme aspects of autumn rainfall in West China, and then projected their changes during the middle and the end of the 21st century under the RCP4.5 scenario. The evaluations indicate a good performance of the RegCM4 downscaling simulations in reproducing the observed spatial distribution of autumn rainfall amount, wet days, maximum consecutive 5‐day precipitation (RX5day), total extremely wet‐day precipitation (R95p), and consecutive dry days (CDD). Under the RCP4.5 scenario, relative to 1986–2005, the amount of autumn rainfall and the frequency of wet days are projected to increase in the northwestern part of West China and to decrease in its southeastern flank, concurrent with greater changes by the end of the 21st century than by the middle of the 21st century. Such an increase is closely associated with the enhancement of zonal water vapour transport and atmospheric unstable stratification, while the projected decrease is largely related to the reduction of moisture supply. Similar changes are also projected for the precipitation extremes Rx5day and R95p. Corresponding to the changing pattern of autumn rainfall, the CDD is projected to decrease in the northwestern part and to increase in the southeastern part of West China. [ABSTRACT FROM AUTHOR]

Two or more spatio-temporally co-located meteorological/climatological extremes (co-occurring extremes) place far greater stress on human and ecological systems than any single extreme could. This was observed during the California drought of 2011–2015 where multiple years of negative precipitation anomalies occurred simultaneously with positive temperature anomalies resulting in California's worst drought on observational record. The large-scale drivers which modulate the occurrence of extremes in two or more variables remains largely unexplored. Using California wintertime (November–April) temperature and precipitation as a case study, we apply a novel, nonparametric conditional probability distribution method that allows for evaluation of complex, multivariate, and nonlinear relationships that exist among temperature, precipitation, and various indicators of large-scale climate variability and change. We find that multivariate variability and statistics of temperature and precipitation exhibit strong spatial variation across scales that are often treated as being homogeneous. Further, we demonstrate that the multivariate statistics of temperature and precipitation are highly non-stationary and therefore require more robust and sophisticated statistical techniques for accurate characterization. Of all the indicators of the large-scale climate conditions we studied, the dipole index explains the greatest fraction of multivariate variability in the co-occurrence of California wintertime extremes in temperature and precipitation. [ABSTRACT FROM AUTHOR]

This study presented an analysis of projection and uncertainty of precipitation extremes over 9 river basins in China based on the outputs of 18 global climate models (GCMs) with 3 Representative Concentration Pathways (RCPs) from the Coupled Model Inter-comparison Project Phase 5 (CMIP5). The temporal and spatial changes in precipitation extremes for the period 2020–2100 were analyzed using the climate indices defined by the Expert Team on Climate Detection and Indices (ETCCDI). Uncertainty of GCMs and RCPs in the projections of precipitation extremes was quantified using the variance-based sensitivity analysis method. The model simulations generally predict a consistent intensification of precipitation extremes during the 21st century with respect to different RCPs scenarios: the higher emission scenarios (e.g., RCP8.5), the larger intensity and frequency of precipitation extremes. The projected changes in precipitation extremes exhibit a large spatial variation across China. The high-latitude and high elevation regions of China (e.g., Continental and Southwest basins) are projected to respond more strongly to the increase in precipitation amount and intensity (e.g., RCPTOT, RX1day and RX5day), while southeastern and southern China (e.g., Southeast and Pearl River basins) tend to be more sensitive to the increase in the frequency of precipitation extreme (e.g., R10mm and R20mm). Consecutive dry days (CDD) is projected to decrease in northern China (e.g., Continental basin) but increase in southern China (e.g., Southeast, Pearl River, and Yangtze River basins). Uncertainty analysis shows that variation of GCMs contributes >90% uncertainty of precipitation extremes projections for the whole China, with larger uncertainty range under the higher emission scenarios (e.g., RCP8.5). The uncertainty from RCPs is generally limited (contribution <10%) in comparison with GCMs, and slightly increases with the long-term projections of precipitation extremes. • Precipitation extremes are projected over 9 river basins in China via 18 GCMs. • Uncertainty of GCMs and RCPs is quantified via variance-based sensitivity method. • Projected changes in precipitation extremes show a large spatial variation. • GCMs contribute >90% uncertainty of precipitation extremes projections. [ABSTRACT FROM AUTHOR]