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For climate risk assessments accurate gridded data sets are needed. An important aspect of such data sets is that they reliably represent the spatial and temporal characteristics of extreme events. This is particularly important for precipitation extreme events which are still not well represented in climate models. Here, we compare South Korean station data with two observation-based gridded data sets (APHRODITE and ERA5-Land) and data from global high-resolution Community Earth System Model (CESM) simulations with an atmospheric resolution of about 25km. We find that the two observation-based data sets have a lower level of the 99th percentile than the station data, but that CESM reproduces extreme events better. Our study provides evidence for an overall historical decrease in very large extreme events in the station data, which is not the case in the two gridded data sets. However, changes in extremes are locally dependent as shown by local quantile regression analysis; where local historical increases in precipitation extremes are statistically significant. The spatial dependence of extreme precipitation events is not well reproduced by the two gridded data sets but well by CESM. The temporal clustering of precipitation extremes is well reproduced by all data sets. Compared to the present day simulation, the CESM simulation of a warmer climate state shows an overall increase in mean precipitation and precipitation extremes and regionally dependent changes in temporal clustering. The model results also provide evidence for a change in spatial dependence in a warmer climate with spatially larger extreme precipitation systems possible. Our results highlight the need to produce better observation-based gridded data sets and also the need to adapt to more intense and frequent extreme precipitation events in the future in South Korea. [ABSTRACT FROM AUTHOR]

Perturbations in extreme precipitation characteristics are investigated over the Volta Basin (VB) and its three subdomains (Sahel, Soudano-Sahel and Guinea Coast) for the early-21st (2030–2053) and mid-twenty-first centuries (2057–2080) under representative concentration pathways (RCPs) 4.5 and 8.5. Seven climate indices from the Expert Team on Climate Change Detection and Indices were selected to examine future extreme precipitation features. Owing to its performance over the West African sub region, CanESM2 model results were used with Global Precipitation Climatology Centre (GPCC v7) dataset serving as reference data. Results generally show lowering trends in extreme precipitation events over the VB. The declines in extremes were dominant in the Sahel and Soudano-Sahel zones with some degree of upsurges observed in the Guinea Coast. Spatially over the basin, wet spells (CWD) were projected to shorten under RCP 8.5 (~ 7–27 days/year) relative to RCP 4.5 (~ 8–30 days/year). Similar pattern was observed for dry spells (CDD) with ranges of ~ 64–198 days/year and ~ 61–186 days/year respectively for RCPs 4.5 and 8.5. As revealed, future alterations in precipitation events have the propensity to cause alternating drought or flood events. In this line, sustainable adaptation measures and coping strategies need to be devised in time to minimize the consequences of these events, particularly those on water resources availability and ecosystem functions and services. [ABSTRACT FROM AUTHOR]

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country's food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980–2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985–2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025–2049, F2: 2050–2074, F3: 2075–2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10 mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future. [ABSTRACT FROM AUTHOR]

Assessing the impacts of anthropogenic land use and land cover change (LULCC) on climate extremes is crucial for the public. This necessitates the utilization of state-of-the-art experiments and datasets to enhance our understanding. Here, we used CMIP6-LUMIP experimental results to show the biogeophysical effects of LULCC on extreme temperature and precipitation during the historical period. Three regions with intense LULCC in the Northern Hemisphere experienced cooling effects caused by LULCC. In contrast, only the highest maximum temperature (TXx) showed slight warming effects in three regions with intense LULCC in the Southern Hemisphere near the tropics. The maximum changes in intensity were an ∼ 0.8 °C decrease in the TXx and nearly a 2 °C decrease in the 90th percentile in the lowest minimum temperature (TNn) in North America. South Asia experienced ∼ 4% and 10-day decreases in warm days (TX90p) and warm spell duration (WSDI), respectively, in the 90th percentile. The precipitation response to LULCC displayed a clear difference between the Northern Hemisphere (wetter) and the Southern Hemisphere (drier), especially in terms of mean precipitation and wet days (R1mm). Previous studies support our findings that the impacts of LULCC on temperature extremes are greater in regions with intense LULCC than in remote areas. Seasonal results showed that March-April-May (MAM) and June-July-August (JJA) contributed more to the maximum temperature changes, while the minimum temperature experienced greater responses in MAM and December-January-February (DJF). LULCC tended to induce a shift toward either warmer and drier conditions or wetter and colder conditions in approximately two-thirds of season–region pairs. [ABSTRACT FROM AUTHOR]

Mountain regions are recognised as hot-spots of climate change. Although the existence of an Elevation-Dependent Warming has been extensively confirmed in several mountain areas of the globe, fewer studies have analysed the elevational stratification of temporal trends of other climate variables, and particularly for precipitation. This study analyses changes in mean precipitation and its extremes in ERA5 global reanalysis data in key mountain areas of the globe, along with their elevational dependence, from 1951 to 2020. These include the Tibetan Plateau, the US Rocky Mountains, the Greater Alpine Region, and the Andes, as representative of different latitudes and climatic influences. Our analysis reveals common patterns of elevational dependent change in precipitation and its extremes in most of the mountainous areas, which emerge beyond their geographical differences. A positive elevational gradient of trends of extreme precipitation indices is found in the Tibetan Plateau, the Greater Alpine Region, and the subtropical Andes, highlighting a wetting effect (positive trends) at very high elevations. In contrast, the Rocky Mountains exhibit a negative elevational gradient, with a drying effect (negative trends) increasing with the elevation. Notably, a simple linear regression proved to be effective to describe the stratification of change in the Greater Alpine Region and the Rocky Mountains, whereas more complex vertical patterns need to be considered for the Andes and the Tibetan Plateau. Mean precipitation, heavy (≥ 10 mm) precipitation and the length of consecutive wet days show a consistent elevation-dependent stratification within each of the study areas, suggesting possible common driving mechanisms. [ABSTRACT FROM AUTHOR]

In the present study, we have made an effort to examine the influence of dust aerosols on the precipitation over India, and six homogeneous rainfall zones of India during pre-monsoon (March to May) and monsoon (June to September) seasons. In this regard, a set of sensitivity experiments are performed using a regional climate model, namely, RegCM-4.7.0, for 11 years. Extensive analysis has been carried out to evaluate the model's performance in simulating the mean precipitation, its variability (spatial and temporal), dust aerosol optical depth, and monsoonal wind. It is observed that the model has the potential to capture the distinct features like prevailing north westerlies with less precipitation during pre-monsoon and southwesterlies with more precipitation during monsoon season, with systematic differences in magnitude while compared against observation. High dust aerosol optical depth is found during pre-monsoon season over central northeast, west central and northeast parts of India. Dust significantly increases precipitation parts over southern peninsula, west central and northwest during the monsoon season due to increases in wind at 850 hPa facilitating moisture transport from Arabian Sea. Interestingly, it is also noticed that dust aerosols modulate the magnitude of extreme precipitation indices over India implying importance in including dust aerosols effects in the climate models. [ABSTRACT FROM AUTHOR]

This study assesses the long-term variability of extreme precipitation events in the Carpathian region of Poland in relation to climate change and its potential impact on river regime. Focusing on eight catchments with undisturbed runoff regimes, the research utilized daily data from May to October between 1951 and 2022. This data included precipitation totals from 23 gauging stations, discharge values from eight water level gauging stations, and gridded precipitation data from the E-OBS dataset. Additionally, future projections for 2026–2060 under two RCP scenarios (4.5 and 8.5) were generated using five GCM-RCM model combinations from the EURO-CORDEX simulations. The study explored the relationship between precipitation and discharge, using probability techniques to identify extreme events. This study highlights the strong linkages between extreme precipitation and discharge in the region, indicating an elevated flood risk due to increased precipitation variability and volume. Despite no significant trends in the frequency of precipitation extremes being observed in the historical analysis, an increase in seasonal precipitation totals is projected, although with considerable uncertainty. [ABSTRACT FROM AUTHOR]

Present study explores the precipitation spectrum over Central India in changing climate using accurate precipitation estimates at fine resolution (hourly observations at 5 km resolution) from multi-spectral satellite observations using 24 years (1998–2021) of high-resolution data of precipitation over India during the summer monsoon. Though only a 24-year short period is perceived to be inadequate to explore the long-term changes in the precipitation spectrum, this study still represents the best possible way to explore the changes in short-lived intense precipitation events in changing climate in the high-resolution satellite era. Changes in precipitation extremes are linked with regional warming over India. A significant increase in heavy precipitation events is observed over Central India. Heavy precipitation events show an increase of about 137% over Central India for a unit degree increase in warming. Low and moderate precipitation events show a decrease of up to 54% over the region. Changes in extreme rain events are highest in July month. Results reported in this study highlight the importance of disaster preparedness against flash flooding due to increased heavy rain events. [ABSTRACT FROM AUTHOR]

Extreme weather events are more detrimental to human culture and ecosystems than typical weather patterns. A multimodel ensemble (MME) of the top-performing global climate models (GCMs) to simulate 11 precipitation extremes was selected using a hybrid method to project their changes in Pakistan. It also compared the benefits of using all GCMs compared to using only selected GCMs when projecting precipitation extremes for two future periods (2020–2059) and (2060–2099) for four shared socioeconomic pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. Results showed that EC-Earth3-Veg, MRI-ESM2-0 and NorESM2-MM performed best among GCMs in simulating historical and projecting future precipitation extremes. Compared to the MME of all GCMs, the uncertainty in future projections of all precipitation indices of the selected GCMs were significantly smaller. The MME median of the selected GCMs showed increased precipitation extremes over most of Pakistan. The greater increases were in RX1day by 6–12 mm, RX5day by 12–20 mm, Prcptot by 40–50 mm, R95ptot by greater than 30 mm, R99ptot by more than 9 mm, R4mm ≥ 4 mm by 0–4 days, R10mm by 2–6 days, R20mm by 1–3 days, and SDII by 1 mm/day, CWD by one day, CDD by 0–4 days in the northern high elevated areas for SSP5-8.5 in the late future. These results emphasize the greater influence of climate change on precipitation extremes in the northern, high-elevation areas, which provide the majority of the country's water. This emphasizes the necessity to adopt suitable climate change mitigation strategies for sustainable development, particularly in the country's northern regions. [ABSTRACT FROM AUTHOR]

Intensity-duration-frequency (IDF) curves are commonly used in engineering practice for the hydraulic design of flood protection infrastructures and flood risk management. IDF curves are simple functions between the rainfall intensity, the timescale at which the rainfall process is studied, and the return period. This work proposes and tests a new methodological framework for the spatial analysis of extreme rainfall depth at different timescales, taking advantage of two different precipitation datasets: local observational and gridded products. On the one hand, the proposed method overcomes or reduces known issues related to observational datasets (missing data and short temporal coverage, outliers, systematic biases and inhomogeneities, etc.). On the other hand, it allows incorporating appropriately terrain dependencies on the spatial distribution of the extreme precipitation regime. Finally, it allows to estimate the IDF curves at regional level overcoming the deficiencies of the classical regional approaches commonly used in practice. The method has been tested to compute IDF curves all over the Basque Country, contrasting results with respect to local analyses. Results show the method robustness against outliers, missing data, systematic biases and short length time series. Moreover, since generalized extreme value (GEV)-parameters from daily gridded dataset are used as covariates, the proposed approach allows coherent spatial interpolation/extrapolation of IDF curves properly accounting for the influence of orographic factors. In addition, due to the current coexistence of local observations and gridded datasets at regional (e.g. The Alps), national (e.g. Spain, France, etc.) or international scale (e.g. E-OBS for Europe or Daymet for the United States of America), the proposed methodology has a wide range of applicability in order to fulfill the known gaps of the observational datasets and reduce the uncertainty related to analysis and characterization of the extreme precipitation regime. [ABSTRACT FROM AUTHOR]

An Emergent Constraint (EC) is a physically-explainable relationship between model simulations of a past climate variable (predictor) and projections of a future climate variable (predictand). If a significant correlation exists between the predictand and the predictor, observations of the latter can be used to constrain model projections of the former and to narrow their uncertainties. In the present study, the EC technique has been applied to the analysis of precipitation, one of the variables most affected by model uncertainties and still insufficiently analysed in the context of ECs, particularly for the recent CMIP6 model ensemble. The main challenge in determining an EC is establishing if the relationship found is physically meaningful and robust to the composition of the model ensemble. Four precipitation ECs already documented in the literature and so far tested only with CMIP3/CMIP5, three of them involving the analysis of extreme precipitation, have been reconsidered in this paper. Their existence and robustness are evaluated using different subsets of CMIP5 and CMIP6 models, verifying if the EC is still present in the most recent ensemble and assessing its sensitivity to the detailed ensemble composition. Most ECs considered do not pass this test: we found one EC not to be robust in both CMIP5 and CMIP6, other two exist and are robust in CMIP5 but not in CMIP6, and only one is verified and is robust in both model ensembles. [ABSTRACT FROM AUTHOR]

The perception of spatio-temporal variability of winter precipitation covering dominant water resources and environment over Kashmir Himalaya is a pre-eminent perspective under current social interests. The main objectives of the study are, firstly, to unriddle the whole (1980–2020) and post-2000 trend in the winter season precipitation (WP), frequency of low-pressure systems (Lp-S), and western disturbances (WDs) to understand the footprints of climate change over the region; secondly, to evaluate the spatio-temporal variability of winter precipitation under the dominance of global teleconnection indices, using both station and reanalysis datasets. Modified Mann-Kendell test and a linear regression model were utilized to extract the trend and significance in WP, Lp-S, and WDs. The results reveal that WP shows an insignificant decreasing trend (1980–2020), with an increasing trend post-2000, while a decreasing trend is seen during post-2000 in the frequency of Lp-S and WDs. The contribution of WP to annual precipitation shows decadal variability with a decreasing trend post-2000. The anatomy of winter extreme precipitation days has shown a decreasing trend (1980–2020), with a robust coupled increasing trend with WP post-2000. The dynamics of ten severe WD-based extreme precipitation events show that a cyclonic circulation and trough connects the Arabian Sea and the Himalayas, bringing additional moisture to the study region and resulting in extreme precipitation. The cogency of global teleconnections on the variability of WP shows that a pattern with positive phase of NAO, ENSO, and a negative phase in Siberian high intensifies the WD-based precipitation. The study discovers its applicability for the regional forecast system in predicting the winters. [ABSTRACT FROM AUTHOR]

Precipitation extremes are a major impact-relevant implication of climate change. Rising temperatures increase the moisture holding capacity at a rate of ≈ 7 % K - 1 , called the Clausius-Claypeyron (CC) scaling, which can lead to intense precipitation which last for short duration. At a regional level, the scaling of extremes deviates from this expected scaling rate. Large scale circulation dynamics and local variability in thermodynamic influences are suspected to cause these deviation, but these drivers differ across seasons. In the present study, we use ERA5 reanalysis to evaluate the seasonal changes in precipitation-temperature scaling rates over the Indian subcontinent. We further determine the deviations from the expected CC scaling rate, and the precipitation extremes are decomposed to their dynamic and thermodynamic contribution across different seasons. It is found that significant seasonal contrast exists in the dynamic and thermodynamic contributions, with the latter dominating during the Indian summer monsoon season, while the former being higher during the pre-monsoon and post-monsoon season. Further analysis highlights that the lower dynamic contribution is attributed to drop in dew point temperatures and Convective Available Potential Energy during extremes. The primary drivers causing the extremes in different seasons are also pointed out, further improving the understanding of how the intensity and frequency of precipitation extremes changes spatially across different seasons, and what are the physical drivers causing these changes. [ABSTRACT FROM AUTHOR]

Purpose: Soil erosion and sediment transport are often induced by a few extreme precipitation events. Currently, there is limited information on the effects of changes in precipitation extremes on river sediment load over a long time period at the basin scale. The objectives of this study were to quantify the effects of variations in precipitation extremes on sediment load in the Second Songhua River Basin (SSRB), northeast China. Materials and methods: Daily precipitation data from 10 meteorological stations within the SSRB and sediment load data during 1960–2014 from the basin outlet hydrological station were collected. Six extreme precipitation indices, such as erosive precipitation (ERPTOT), wet-day precipitation (PRCPTOT), very wet day precipitation (R95pTOT), flood season precipitation (FSPTOT), maximum 1-day precipitation (RX1day) and heavy precipitation (HP), were used to detect the precipitation extremes. The Mann–Kendall non-parametric trend analysis was used to analyze the dynamic changes in precipitation extremes and sediment load. The double cumulative curve method was used to assess the effects of changes in precipitation extremes on sediment load. Results and discussion: The results indicated that six extreme precipitation indices showed periodic variations from 1960 to 2014, while they did not show any significant change trend in the SSRB (p > 0.05); however, the sediment load showed an extremely significant decreasing trend (p < 0.01) over the past 55 years. Sediment load significantly correlated with six precipitation extremes indices (p < 0.05). Compared with the baseline period (1960–1977), the sediment load was reduced by 1.12 × 106 ton per year (1977–2014). The contribution of precipitation extremes to the sediment load ranged from -22.83% to 6.97%. The corresponding contribution of the non-precipitation factor to sediment load was from 93.03% to 122.83%. Conclusions: The change in precipitation extremes was not the main reason for the sediment load reduction. Sediment load reduction in the SSRB was due to the interactions between multiple factors. Non-precipitation factors, such as reservoir construction, were the main reasons for the sediment load reduction. These findings are useful to understand the variation of river sediment load and to adopt countermeasures to relieve potential risk of precipitation extremes. [ABSTRACT FROM AUTHOR]

Global warming has altered the thermodynamic and dynamic environments of climate systems, affecting the biogeochemical processes between the geosphere and atmosphere, which has significant impacts on precipitation extremes and the terrestrial carbon budget of ecosystems. Existing studies have reported a hook structure for precipitation extreme-temperature relationships but have rarely examined the underlying physical mechanisms. Previous studies have also failed to quantify the impact of precipitation on ecosystem productivity, hindering the assessment of future extreme climatic hazards and potential ecosystem risks. To reveal the thermodynamic driving mechanisms for the formation of global precipitation extremes and ecohydrological effects, this study utilizes over ten multisource datasets (i.e., satellite, reanalysis, climate model, land surface model, machine learning reconstruction, and flux tower measurements). We first assess the response of water-heat-carbon flux to precipitation extremes and explain the underlying physical mechanisms behind the hook structures in terms of atmospheric thermodynamics and dynamics. Based on outputs from five global climate models (GCMs) under ISIMIP3b, we project future changes in the hook structures as well as their impacts on precipitation extremes. Finally, we discuss the impact of precipitation on the terrestrial carbon budget by using outputs from the CLM4.5 model. The results show that precipitation extremes are usually accompanied by strong exchanges of water and heat and demonstrate a nonlinear relationship between precipitation and ecosystem productivity. The intensity (duration) of extreme precipitation is intensifying (decreasing) over most areas of the globe, whereas three-dimensional precipitation events are becoming more concentrated. Atmospheric dynamics play a key role in shaping the hook structure. The structure is not stable; it shifts under climate change and is projected to result in a 10–10% intensification in precipitation by the end of this century. Moderate levels of precipitation contribute to carbon assimilation in ecosystems, and the response of the carbon budget to precipitation is relatively stable under climate change. [ABSTRACT FROM AUTHOR]

Studying changes in precipitation extremes are important due to the possible increase in heavy rainfall, drought, and the occurrence of summer storms. Therefore, in this study, the trend of variations in precipitation extremes in the Middle East and North Africa (MENA) in the historical period (1985–2014) and the future period (2021–2050) was investigated. For this purpose, five precipitation extremes indices, including R10mm, R20mm, R95p, R99p, and PRCPTOT, and modified Man–Kendall trend test were used to examine the changes. To project changes in the future period, the multi-model mean of 12 GCMs from CMIP6 under three Shared Socioeconomic Pathway (SSP) scenarios including SSP1-2.6, SSP3-7.0, and SSP5-8.5 was utilized. The spatial distribution of the trend of changes in precipitation limits in the historical period showed that by moving from north to south and from west to east of the MENA region, all the studied indices have an increasing trend, so that the highest rate of decline is related to the countries of North Africa and also the PRCPTOT index and in Algeria, with a modified Man–Kendall coefficient of − 2.9. The highest increasing trend of the studied indices is related to the Middle East countries, especially countries such as Iran, Saudi Arabia, and Iraq with a modified Man–Kendall coefficient between 1.1 and 2.4, depending on the various indices. The distribution of variation trend in the future period also showed that the trends are similar to the historical period, with the difference that the downward trends in the studied indices are adjusted and the upward trends, especially based on the SSP5-8.5 scenario, would be intensified. Thus, the annual total precipitation on wet days (PRCPTOT) decreased and the annual count of days with 10- and 20-mm precipitation (R10mm and R20mm) as well as annual total PRCP with 95th and 99th percentiles (R95p and R99p) increased, with the largest increase in the Middle East and East of the MENA region; which indicates that despite the decrease in precipitation amount, the precipitation extremes and their intensities will increase. [ABSTRACT FROM AUTHOR]

The Himalayan region of Uttarakhand, India, has witnessed floods and landslides, and more extremes are likely in the future. This study examined the projected changes in precipitation extremes by using state-of-the-art, high-resolution (0.25° × 0.25°) statistically downscaled NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) during southwest monsoon season (June to September) under the RCP 4.5 and RCP 8.5 scenarios. The spatial variations of mean precipitation, as well as the extremes obtained from the multi-model mean (MMM) from NEX-GDDP simulations, were compared with Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) observational data for the baseline period (1976–2005). For the future climate, the monsoon precipitation over the Uttarakhand region may increase by ~ 13% and ~ 16% under the RCP 4.5 and RCP 8.5 emission scenarios, respectively, in the near future (2021–2050) and may further increase by 23% and 36% in the far future (2070–2099). The different extreme precipitation indices analyzed show an increase with the exception of consecutive dry days (CDDs) over Uttarakhand, both in the near future and in the far future, under both scenarios. The RCP 4.5 and RCP 8.5 scenarios exhibited a noticeable increase in the highest 1-day rainfall (by 1.4 mm decade−1 and 3.3 mm decade−1) and in the highest 5-day rainfall (by 2.7 mm decade−1 and 7 mm decade−1), along with the extreme R95P precipitation days (by 11% and 22%), and consecutive wet days become more frequent during monsoon season, respectively. The study findings highlight the need for considering more extreme rains in base Himalayan climate resiliency planning. [ABSTRACT FROM AUTHOR]

Ethiopia witnessed tremendous precipitation variability and extremes linked with large-scale climate anomalies. This study investigated the long-term spatiotemporal trends of precipitation extremes, significant change points, and its teleconnection to climate anomalies. We used a daily CHIRPS gridded precipitation dataset of the past four decades covering from 1981 to 2019. Eight extreme precipitation indices are defined here based on Expert Team on Climate Change Detection and Indices guidelines. We used the Mann–Kendall test, Sen's slope estimator, and Pettitt's test to investigate trends of the precipitation change in terms of the magnitude and change point of time series. Wavelet coherence and correlation coefficient are used to identify the relationship between precipitation extremes and climate indices. Our results show a significantly decreasing trend for the Kiremt season (June to Sept) and Belg season (Feb to May) over southeast Ethiopia. The majority of grid points experience a change in time series during 1990 to 2012. Most precipitation extreme indices show an increasing trend over the south and southwest region, except consecutive wet days (CWD), which shows a decreasing trend at similar locations. The multiscale analysis presents strong coherence between precipitation anomaly and Nino 3.4 and IOD over the south and southeast region. Similarly, spatial correlation shows that IOD and Nino 3.4 are positively correlated to R10mm, R25mm, PRCTOT, Rx5day, R95ptot, and R99ptot over south, southwest, and southeast parts of the country. A negative correlation is observed with CDD for similar locations along with NAO climate index for most precipitation extremes. [ABSTRACT FROM AUTHOR]

Extreme precipitation events bring considerable risks to the natural ecosystem and human life. Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical for the flood prevention and water resources planning and management. In this study, daily precipitation data (1957–2019) were collected from 24 meteorological stations in the Weihe River Basin (WRB), Northwest China and its surrounding areas. We first analyzed the spatial-temporal change of precipitation extremes in the WRB based on space- time cube (STC), and then predicted precipitation extremes using long short-term memory (LSTM) network, auto-regressive integrated moving average (ARIMA), and hybrid ensemble empirical mode decomposition (EEMD)-LSTM-ARIMA models. The precipitation extremes increased as the spatial variation from northwest to southeast of the WRB. There were two clusters for each extreme precipitation index, which were distributed in the northwestern and southeastern or northern and southern of the WRB. The precipitation extremes in the WRB present a strong clustering pattern. Spatially, the pattern of only high-high cluster and only low-low cluster were primarily located in lower reaches and upper reaches of the WRB, respectively. Hot spots (25.00%–50.00%) were more than cold spots (4.17%–25.00%) in the WRB. Cold spots were mainly concentrated in the northwestern part, while hot spots were mostly located in the eastern and southern parts. For different extreme precipitation indices, the performances of the different models were different. The accuracy ranking was EEMD-LSTM-ARIMA > LSTM > ARIMA in predicting simple daily intensity index (SDII) and consecutive wet days (CWD), while the accuracy ranking was LSTM > EEMD-LSTM-ARIMA > ARIMA in predicting very wet days (R95P). The hybrid EEMD-LSTM-ARIMA model proposed was generally superior to single models in the prediction of precipitation extremes. [ABSTRACT FROM AUTHOR]

The performance of bias-corrected precipitation of the Coupled Model Intercomparison Project Phase 6 (CMIP6) was evaluated using gauge observations from 45 synoptic stations. The spatial distribution and anomaly of two absolute thresholds of heavy (R10mm), very heavy (R20mm), and two relative thresholds of very wet days (R95p) and extremely wet days (R99p) of precipitation were investigated for the historical period (1975–2014) and two future periods (2021–2060 and 2061–2100) in Iran. Five CMIP6 models were evaluated, including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL by using root mean square error (RMSE), mean bias error (MBE), percent bias (PBIAS), correlation coefficient (PCC) statistics, and receiver operating characteristic (ROC) analysis. An ensemble of the models was generated by applying the independent weighted mean (IWM) method. Among the five models, maximum overestimation and minimum underestimation of precipitation amount are seen in IPSL-CM6A-LR and UKESM1-0-LL, respectively. The maximum R10mm and R20mm computed 35.10 and 18.50 days/year for the historical period, which were identified over the south of the Caspian Sea and Zagros Mountain chain. The anomaly of R10mm and R20mm in all river basins of Iran is positive, except for the four basins of Karkheh, Karun, Tashk-Bakhtegan, and Maharloo for the SSP5-8.5. Precipitation thresholds for both indices will be increasing over the coming decades on the coast of the Caspian Sea, while the index for R95p in the western and southwestern regions and R99p in the southern regions of Iran will experience a decreasing trend. [ABSTRACT FROM AUTHOR]

The urban heat island is a representative urban climate characteristic, which can affect heat-stress conditions and extreme precipitation that are closely connected with human life. Better understanding of urban-climate interactions, therefore, is crucial to ultimately support better planning and adaptation in various application fields. This study assesses urban-climate interactions during summer for eastern North America using regional climate model simulations at 0.22° resolution. Two regional climate model experiments, with and without realistic representation of urban regions, are performed for the 1981–2010 period. Comparison of the two experiments shows higher mean temperatures and reduced mean precipitation in the simulation with realistic urban representation, which can be attributed primarily to reduced albedo and soil moisture for the urban regions in this simulation. Furthermore, the mean temperature and precipitation in the simulation with improved urban representation is also closer to that observed. Analysis of short-duration precipitation extremes for climatologically different sub-regions, however, suggests that, for higher temperatures, the magnitudes of precipitation extremes are generally higher in the simulation with realistic urban representation, particularly for coastal urban regions, and are collocated with higher values of convective available potential energy and cloud fraction. Enhanced sea and lake breezes associated with lower sea level pressure found around these regions, contribute additional water vapor and further enhance dynamic convective development, leading to higher precipitation intensities. Analysis of temperature extremes clearly demonstrates that urban regions experience aggravated heat-stress conditions due to relatively higher temperatures despite reduced relative humidity. Double the number of extreme heat spells lasting six or more days are noted for the coastal urban regions in the study domain. This study, in addition to demonstrating the differences in urban-climate interactions for climatologically different regions, also demonstrates the need for better representation of urban regions in climate models to generate realistic climate information. [ABSTRACT FROM AUTHOR]

East Africa is so vulnerable to the impacts of precipitation extremes varying from frequent floods to prolonged droughts. However, systematic regional assessment of precipitation extremes across seasons has received little attention, and most previous studies of precipitation extremes have employed many indices and sparse gauge observations giving marginalized details. In this study, we use three precipitation extreme indices to examine the intensity of the highest single-day rainfall record (rx1day), prevalence of very heavy rainfalls (r30mm), and persistence of successive wet days (cwd) at both annual and seasonal scales over recent two decades (1998–2018) based on the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis data. The results show that the most intensive and frequent precipitation extremes are noticeable from January to May across the areas extending from Madagascar to the Tanzanian coastal zone. These areas also exhibit patches of significant increasing trends in frequency, duration, and intensity of precipitation extremes annually and seasonally. However, significant declines in frequency and intensity of precipitation extremes are observed from western Ethiopia to Congo-Uganda, especially in June-September. The October–December season witnesses higher interannual variability amounting to overall weak and less significant trends. Further subregional assessment shows overall declining intensity and frequency of precipitation extremes in northern part of the study areas. Mean wetness duration increased, with persistence of moderate wet days and slight reduction of severe events. This study unveils higher susceptibility of the East African region to the widely observed hotspots of precipitation extremes posing threats to food security, water resource, and human well-being. The region should consider upscaling irrigation schemes in addition to planning resilient and supportive infrastructures to withstand the upsurging precipitation extremes, especially along the coastal zone. [ABSTRACT FROM AUTHOR]

The frequency and spatial distributions of precipitation extremes (PEs) and deep convective clouds (DCC) across the Amazon region were assessed using satellite-derived data. For PEs, CHIRPS dataset for the period 1981–2018 were used to calculate a set of absolute, threshold, duration, and percentile-based threshold indices defined by the Expert Team on Climate Change Detection and Indices. DCC occurrence was assessed based on the Advanced Microwave Sounding Unit data for the period 2002–2018. In northern Amazon (north of 5 ∘ S ) PEs and DCC are more frequent ( ≥ 60 % frequency) during February–June. Averaged trends over these months have shown increase in daily rainfall above 20 mm of near 3 days over the 1981–2018 period, and an increase of 2 consecutive wet days (P ≥ 1 mm ) in the same period. South of 5 ∘ S prevalence of PEs and DCC is largely observed during November–March ( ≥ 60 % frequency), whereas the longest persistence of dry days is observed during June–August. Though all PE trends point to an intensification of rainfall in November–March, only consecutive dry days in winter (JJA) and spring (SON) show significant trends, pointing to an increase of 7 days over the 38-yr winters. Rainfall extremes over the entire Amazon region were found to be moderate to strongly correlated with the mean vertically integrated moisture divergence, and in southern Amazon also to upper level divergence and upward vertical velocity. Increased frequency of DCC were found over the whole basin ( ∼ 18 % yr - 1 ), in contrast to decreased convective overshooting (up to ∼ 15.4 % yr - 1 ). [ABSTRACT FROM AUTHOR]

There is a large agreement that global warming induces changes of precipitation regimes of different nature and amplitude depending on the timescale considered. This question is of special concern regarding extreme rainfall that might have critical socio-environmental consequences. A unified framework is proposed here for detecting trends in extreme rainfall. It is based on the GEV distribution, whose parameters depend both on a simple scaling formulation to account for multiple time durations of rainfall and on time to account for the non-stationarity deriving from climatic trends. The implementation of the model is illustrated in the Sahel region by analyzing 30 in situ rainfall series of 28 years measured at time-steps from 2 to 24 h. While the separate analysis of the point series proves inconclusive for detecting trends at any of the time-steps considered, the inclusion of all the series and time-steps into the proposed unified model allows trends to be detected at a high level of confidence (p-value < 1%). This trend essentially appears in the scale parameter of the regional GEV distribution, involving a 15 to 20% increase of the 10-year rainfall in 28 years, and a 23 to 30% increase of the 100-year rainfall. The main advantages of the proposed framework are (i) its parsimony, allowing for reducing the uncertainty associated with the model inference; (ii) its capacity for detecting trends either in the mean and/or in the variability of the extreme events; and (iii) its ability for producing non-stationary Intensity-Duration-Frequency curves that are coherent over a range of durations of accumulation. [ABSTRACT FROM AUTHOR]

In the study of diagnosing climate simulations and understanding the dynamics of precipitation extremes, it is an essential step to adopt a simple model to relate water vapor condensation and precipitation, which occur at cloud-microphysical and convective scales, to large-scale variables. Several simple models have been proposed; however, improvement is still needed in both their accuracy and/or the physical basis. Here, we propose a two-plume convective model that takes into account the subgrid inhomogeneity of precipitation extremes. The convective model has three components, i.e., cloud condensation, rain evaporation, and environmental descent, and is built upon the zero-buoyancy approximation and guidance from the high-resolution reanalysis. Evaluated against the CMIP5 climate simulations, the convective model shows large improvements in reproducing precipitation extremes compared to previously proposed models. Thus, the two-plume convective model better captures the main physical processes and serves as a useful diagnostic tool for precipitation extremes. [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]

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]

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]

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]

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]

Globally, mean and extreme precipitation will increase with climate change. This is largely controlled by moisture and energy availability which is linked to temperature. Therefore, changes in precipitation are regularly presented proportional to a change in mean global temperature, and temperature is often proposed as a covariate for projecting precipitation with climatic change. However, studies which investigate the association between precipitation and temperature largely focus on the day-to-day association between precipitation and temperature fluctuations at a gauged location, which is not necessarily equivalent to changes in precipitation at climatic spatial and time scales. To assess whether temperature changes may help inform changes in precipitation with climatic change, we evaluate the historical relationship between precipitation and annual temperature fluctuations. We find positive correlations between precipitation and mean annual dew point temperature. These associations are strongest for annual average precipitation and weakest for the shortest, most extreme precipitation. We find that the strength of this correlation is more strongly linked to the number of rain days, rather than the precipitation depth itself. When dry-bulb temperatures are used in place of dew point temperature, the association between precipitation and temperature is either negative or zero. As a strong association between wet day dry-bulb and dew point temperatures exists, changes in temperature may aid in understanding the changes to precipitation as global temperatures increase. However, as the precipitation-dew point correlation is not necessarily physically related to the precipitation depth but rather to precipitation occurrence; precipitation-temperature sensitivities need to be interpreted with caution. [ABSTRACT FROM AUTHOR]

In response to the impacts of extreme precipitation on human or natural systems under climate change, the development of climate risk assessment approach is a crucial task. In this paper, a novel risk assessing approach based on a climate risk assessment framework with copula-based approaches is proposed. Firstly, extreme precipitation indices (EPIs) and their marginal distributions are estimated for historical and future periods. Next, the joint probability distributions of extreme precipitation are constructed by copula methods and tested by goodness-of-fit indices. The future joint probabilities and joint return periods (JRPs) of the EPIs are then evaluated. Finally, change rates of JRPs for future periods are estimated to assess climate risk with the quantitative data of exposure and vulnerability of a protected target. An actual application in Taiwan Island is successfully conducted for climate risk assessment with the impacts of extreme precipitation. The results indicate that most of regions in Taiwan Island might have higher potential climate risk under different scenarios in the future. The future joint probabilities of precipitation extremes might cause the high risk of landslide and flood disasters in the mountainous area, and of inundation in the plain area. In sum, the proposed climate risk assessing approach is expected to be useful for assisting decision makers to draft adaptation strategies and face high risk of the possible occurrence of natural disasters. [ABSTRACT FROM AUTHOR]

The role of anthropogenic aerosols in future projections (up to 2100) of summertime precipitation and precipitation extremes over the Asian monsoon region is investigated, by comparing two sets of the Community Earth System Model (CESM1) large ensemble simulations under the Representative Concentration Pathway 8.5 scenario (RCP8.5) and the corresponding scenario with aerosol fixed at 2005 levels (RCP8.5_FixA). The model is verified to be performing well in capturing present-day (1986–2005) climate and precipitation extremes. Our results suggest that the Asian monsoon region would become progressively warmer and wetter in the future under RCP8.5, while precipitation extremes will be significantly aggravated due to anthropogenic aerosol mitigation, particularly over East Asia. Specifically, aerosol reductions are found to shift the distribution of precipitation mean and extremes to larger values. For example, aerosol reductions would result in an increased likelihood of extreme precipitation (e.g. the maximum consecutive 5-day precipitation amount) and related disasters. Sensitivities of changes in precipitation mean and extremes to local warming from aerosol reductions are much larger than that from greenhouse gas increases. This is particularly important over East Asia in accordance with larger magnitudes of aerosol reductions compared to South Asia. Finally, by investigating the response of the climate system to aerosol changes, our findings demonstrate that aerosol induced precipitation changes would be dominated by aerosol–radiation–cloud forcing over northern East Asia and aerosol forcing induced large-scale circulation anomalies over southern East and South Asia. [ABSTRACT FROM AUTHOR]

This study aimed to analyze extreme temperature and precipitation indices at seven stations in the Marmara Region of Turkey for the period 1961-2016. The trend of temperature indices showed that the warm-spell duration and the numbers of summer days, tropical nights, warm nights, and warm days have increased, while the cold-spell duration and number of ice days, cool nights, and cool days have decreased across the Marmara Region. Additionally, the diurnal temperature range has slightly increased at most of the stations. A majority of stations have shown significant warming trends for warm days and warm nights throughout the study area, whereas warm extremes and night-time based temperature indices have shown stronger trends compared to cold extremes and day-time indices. The analysis of precipitation indices has mostly shown increasing trends in consecutive dry days and increasing trends in annual rainfall, rainfall intensity for inland and urban stations, especially for stations in Sariyer and Edirne, which are affected by a fast rate of urbanization. Overall, a large proportion of study stations have experienced an increase in annual precipitation and heavy precipitation events, although there was a low percentage of results that was significant. Therefore, it is expected that the rainfall events will tend to become shorter and more intense, the occurrence of temperature extremes will become more pronounced in favor of hotter events, and there will be an increase in the atmospheric moisture content over the Marmara Region. This provides regional evidence for the importance of ongoing research on climate change. [ABSTRACT FROM AUTHOR]

A warmer climate is projected for mid-Europe, with less precipitation in summer, but with intensified extremes of precipitation and near-surface temperature. However, the extent and magnitude of such changes are associated with creditable uncertainty because of the limitations of model resolution and parameterizations. Here, we present the results of convection-permitting regional climate model simulations for Germany integrated with the COSMO-CLM using a horizontal grid spacing of 1.3 km, and additional 4.5- and 7-km simulations with convection parameterized. Of particular interest is how the temperature and precipitation fields and their extremes depend on the horizontal resolution for current and future climate conditions. The spatial variability of precipitation increases with resolution because of more realistic orography and physical parameterizations, but values are overestimated in summer and over mountain ridges in all simulations compared to observations. The spatial variability of temperature is improved at a resolution of 1.3 km, but the results are cold-biased, especially in summer. The increase in resolution from 7/4.5 km to 1.3 km is accompanied by less future warming in summer by 1 ∘C. Modeled future precipitation extremes will be more severe, and temperature extremes will not exclusively increase with higher resolution. Although the differences between the resolutions considered (7/4.5 km and 1.3 km) are small, we find that the differences in the changes in extremes are large. High-resolution simulations require further studies, with effective parameterizations and tunings for different topographic regions. Impact models and assessment studies may benefit from such high-resolution model results, but should account for the impact of model resolution on model processes and climate change. [ABSTRACT FROM AUTHOR]

Changes in climate extremes may cause the variation of occurrence and intensity of floods and droughts. To investigate the future changes in joint probability behaviors of precipitation extremes for water resources management, an approach including three stages for analyzing the spatial variation of joint return periods of precipitation extremes is proposed in this paper. In the first stage, a weather generator model (WGM) was conducted with general circulation models (GCMs) under representative concentration pathway (RCP) scenarios to generate daily rainfall time series during 2021-2040 (S) and 2081-2100 (L) based on the statistics of the observed rainfall data. Four extreme precipitation indices are defined to represent extreme precipitation events. In the second stage, copula methods are adopted to establish the joint distribution of the precipitation extreme indices. The watershed-scale assessment of flood and drought applied in Shih-Men reservoir in northern Taiwan is conducted to demonstrate the possible change of joint return period. In the third stage, the change rates of joint return periods for bivariate extreme indices are demonstrated to present the occurrence possibility of floods or droughts in the future. The results indicate that floods and droughts might occur more frequently in the upstream region of the reservoir during the twenty-first century. The reservoir operations would be more important for water supply and flood mitigation. In conclusion, the possible changes of future joint probability of the precipitation extremes should be paid attention to for water resources management and draft plans to confront potential challenges in the future. [ABSTRACT FROM AUTHOR]

This contribution explores the evolution of the flood risk in the Metropolitan Area of Barcelona (MAB; Northeast Spain) from 1981 to 2015, and how it has been affected by changes in land use, population and precipitation. To complete this study, we analysed PRESSGAMA and INUNGAMA databases to look for all the information related to the floods and flash floods that have affected the chosen region. The “Consorcio de Compensación de Seguros”, a state insurance company for extraordinary risks, provided data on economic damage. The extreme precipitation trend was analysed by the Fabra Observatory and El Prat-Airport Observatory, and daily precipitation data were provided by the State Meteorological Agency of Spain (AEMET) and the Meteorological Service of Catalonia (SMC). Population data were obtained from the Statistical Institute of Catalonia (IDESCAT). Changes in land use were estimated from the land use maps for Catalonia corresponding to 1956, 1993, 2000, 2005 and 2009. Prevention measures like rainwater tanks and improvements to the drainage system were also been considered. The specific case of Barcelona is presented, a city recognised by United Nations International Strategy for Disaster Reduction as a model city for urban resilience to floods. The evolution of flood events in the MAB does not show any significant trend for this period. We argue that the evolution in floods can be explained, at least in part, by the lack of trend in extreme precipitation indices, and also by the improvements in flood prevention measures. [ABSTRACT FROM AUTHOR]

This paper concerns our approach to the EVA2017 challenge, the aim of which was to predict extreme precipitation quantiles across several sites in the Netherlands. Our approach uses a Bayesian hierarchical structure, which combines Gamma and generalised Pareto distributions. We impose a spatio-temporal structure in the model parameters via an autoregressive prior. Estimates are obtained using Markov chain Monte Carlo techniques and spatial interpolation. This approach has been successful in the context of the challenge, providing reasonable improvements over the benchmark. [ABSTRACT FROM AUTHOR]

In this study we investigate the scaling of precipitation extremes with temperature in the Mediterranean region by assessing against observations the present day and future regional climate simulations performed in the frame of the HyMeX and MED-CORDEX programs. Over the 1979-2008 period, despite differences in quantitative precipitation simulation across the various models, the change in precipitation extremes with respect to temperature is robust and consistent. The spatial variability of the temperature-precipitation extremes relationship displays a hook shape across the Mediterranean, with negative slope at high temperatures and a slope following Clausius-Clapeyron (CC)-scaling at low temperatures. The temperature at which the slope of the temperature-precipitation extreme relation sharply changes (or temperature break), ranges from about 20 °C in the western Mediterranean to <10 °C in Greece. In addition, this slope is always negative in the arid regions of the Mediterranean. The scaling of the simulated precipitation extremes is insensitive to ocean-atmosphere coupling, while it depends very weakly on the resolution at high temperatures for short precipitation accumulation times. In future climate scenario simulations covering the 2070-2100 period, the temperature break shifts to higher temperatures by a value which is on average the mean regional temperature change due to global warming. The slope of the simulated future temperature-precipitation extremes relationship is close to CC-scaling at temperatures below the temperature break, while at high temperatures, the negative slope is close, but somewhat flatter or steeper, than in the current climate depending on the model. Overall, models predict more intense precipitation extremes in the future. Adjusting the temperature-precipitation extremes relationship in the present climate using the CC law and the temperature shift in the future allows the recovery of the temperature-precipitation extremes relationship in the future climate. This implies negligible regional changes of relative humidity in the future despite the large warming and drying over the Mediterranean. This suggests that the Mediterranean Sea is the primary source of moisture which counteracts the drying and warming impacts on relative humidity in parts of the Mediterranean region. [ABSTRACT FROM AUTHOR]

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—‘noise’, intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability. [ABSTRACT FROM AUTHOR]

There has been focus on the influence of climate indices on precipitation extremes in the literature. Current study presents the evaluation of the precipitation-based extremes in Columbia River Basin (CRB) in the Pacific Northwest USA. We first analyzed the precipitation-based extremes using statistically (ten GCMs) and dynamically downscaled (three GCMs) past and future climate projections. Seven precipitation-based indices that help inform about the flood duration/intensity are used. These indices help in attaining first-hand information on spatial and temporal scales for different service sectors including energy, agriculture, forestry etc. Evaluation of these indices is first performed in historical period (1971-2000) followed by analysis of their relation to large scale tele-connections. Further we mapped these indices over the area to evaluate the spatial variation of past and future extremes in downscaled and observational data. The analysis shows that high values of extreme indices are clustered in either western or northern parts of the basin for historical period whereas the northern part is experiencing higher degree of change in the indices for future scenario. The focus is also on evaluating the relation of these extreme indices to climate tele-connections in historical period to understand their relationship with extremes over CRB. Various climate indices are evaluated for their relationship using Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). Results indicated that, out of 13 climate tele-connections used in the study, CRB is being most affected inversely by East Pacific (EP), Western Pacific (WP), East Atlantic (EA) and North Atlaentic Oscillation (NAO). [ABSTRACT FROM AUTHOR]

This study investigates the relationship between historically observed changes in extreme precipitation magnitudes and temperature (Pex-T relationship) at multiple locations in Canada. The focus is on understanding the behavior of these relationships with regards to key storm characteristics such as its duration, season of occurrence, and location. To do so, three locations are chosen such that they have large amounts of moisture available near them whereas four locations are chosen such that they are located in the land-locked regions of Canada and subsequently have no nearby moisture source available on them. To investigate the effect of different storm durations on Pex-T relationship, storms of durations: 5, 10, 15, 30 min, 1, 2, 6, 12, 24 h are considered. Finally, Pex-T relationship is analyzed separately for summer and winter seasons to quantify the influence of seasons. Results indicate strong influences of storm duration, season of occurrence, and location on observed precipitation scaling rates. Drastic intensification of precipitation extremes with temperature is obtained for shorter duration precipitation events than for longer duration precipitation events, in summers than in the winters. Furthermore, in summertime, increases in the intensity of convection driven precipitation extremes is found highest at locations away from large waterbodies. On the other hand, in wintertime most drastic increases in extreme precipitation are obtained at locations near large waterbodies. These findings contribute towards increasing the current understanding of precipitation extremes in the context of rapidly increasing global temperatures. [ABSTRACT FROM AUTHOR]

In this study, the trends and periodicity in climate extremes are examined in Hunan Province over the period 1960-2013 on the basis of 27 extreme climate indices calculated from daily temperature and precipitation records at 89 meteorological stations. The results show that in the whole province, temperature extremes exhibit a warming trend with more than 50% stations being statistically significant for 7 out of 16 temperature indices, and the nighttime temperature increases faster than the daytime temperature at the annual scale. The changes in most extreme temperature indices show strongly coherent spatial patterns. Moreover, the change rates of almost all temperature indices in north Hunan are greater than those of other regions. However, the statistically significant changes in indices of extreme precipitation are observed at fewer stations than in extreme temperature indices, forming less spatially coherent patterns. Positive trends in indices of extreme precipitation show that the amount and intensity of extreme precipitation events are generally increasing in both annual and seasonal scales, whereas the significant downward trend in consecutive wet days indicates that the precipitation becomes more even over the study period. Analysis of changes in probability distributions of extreme indices for 1960- 1986 and 1987-2013 also demonstrates a remarkable shift toward warmer condition and increasing tendency in the amount and intensity of extreme precipitation during the past decades. The variations in extreme climate indices exhibit inconstant frequencies in the wavelet power spectrum. Among the 16 temperature indices, 2 of them show significant 1-year periodic oscillation and 7 of them exhibit significant 4- year cycle during some certain periods. However, significant periodic oscillations can be found in all of the precipitation indices.Wet-day precipitation and three absolute precipitation indices show significant 1-year cycle and other seven provide significant power at the 4-year period, which are mainly found during 1970-1980 and after 1992. [ABSTRACT FROM AUTHOR]

With the increasing risk of meteorological disasters, it is of great importance to analyze the spatiotemporal changes of precipitation extremes and its possible impact on rice productivity, especially in Jiangsu province, southeast China. In this study, we explored the relationships between rice yield and extreme precipitation indices using Mann-Kendall trend test, Pettitt's test, and K-means clustering methods. This study used 10 extreme precipitation indices of the rice growing season (May to October) based on the daily precipitation records and rice yield data at 52 meteorological stations during 1961-2012 in Jiangsu province. The main findings were as follows: (1) correlation results indicated that precipitation extremes occurred in the months of July, August, and October, which had noticeable adverse effects on rice yield; (2) the maximum 7-day precipitation of July and the number of rainy days of August and October should be considered as three key indicators for the precipitation-induced rice meteorological disasters; and (3) most of the stations showed an increasing trends for the maximum 7-day precipitation of July and the number of rainy days of August, while the number of rainy days of October in all the stations demonstrated a decreasing trend. Moreover, Jiangsu province could be divided into two major sub-regions such as north and south areas with different temporal variations in the three key indicators. [ABSTRACT FROM AUTHOR]

The effects of climate change on April-October short- and long-duration precipitation extremes over the Canadian Prairie Provinces were evaluated using a multi-Regional Climate Model (RCM) ensemble available through the North American Regional Climate Change Assessment Program. Simulations considered include those performed with six RCMs driven by the National Centre for Environmental Prediction (NCEP) reanalysis II product for the 1981-2000 period and those driven by four Atmosphere-Ocean General Circulation Models (AOGCMs) for the current 1971-2000 and future 2041-2070 periods (i.e. a total of 11 current-to-future period simulation pairs). A regional frequency analysis approach was used to develop 2-, 5-, 10-, 25-, and 50-year return values of precipitation extremes from NCEP and AOGCM-driven current and future period simulations that respectively were used to study the performance of RCMs and projected changes for selected return values at regional, grid-cell and local scales. Performance errors due to internal dynamics and physics of RCMs studied for the 1981-2000 period reveal considerable variation in the performance of the RCMs. However, the performance errors were found to be much smaller for RCM ensemble averages than for individual RCMs. Projected changes in future climate to selected regional return values of short-duration (e.g. 15- and 30-min) precipitation extremes and for longer return periods (e.g. 50-year) were found to be mostly larger than those to the longer duration (e.g. 24- and 48-h) extremes and short return periods (e.g. 2-year). Overall, projected changes in precipitation extremes were larger for southeastern regions followed by southern and northern regions and smaller for southwestern and western regions of the study area. The changes to return values were also found to be statistically significant for the majority of the RCM-AOGCM simulation pairs. These projections might be useful as a key input for the future planning of urban drainage infrastructure and development of strategic climate change adaptation measures. [ABSTRACT FROM AUTHOR]

Arsenic concentrations in a drinking water reservoir system in the Eastern Ore mountains (Osterzgebirge, Germany) were observed over a 17-year period. The region experienced an environmental change during the past 20 years with decreasing acid, sulphur and nitrogen deposition and a recovering vitality of forested catchment sites. An increase of the arsenic content in the reservoir waters during that change was observed. This was caused by a diminished nitrate supply leading to lower redox potential in the sediments favouring sediment arsenic release. The recent annual cycle in the Altenberg reservoir water arsenic concentration was found to be independent from artificial aeration of the hypoxic hypolimnion during the summer stratification. However, we found a strong seasonal dependent change in water As concentration, with a maximum in autumn and a minimum in spring. The low productive system is driven by peat derived organic matter. For the recent arsenic catchment yield coherencies to dissolved organic carbon export and runoff intensity were found, indicating rising arsenic loads due to climate-related soil organic matter destabilization. Thus, in the reservoir system, both dry and wet climate conditions can increase the water As concentrations due to an internal arsenic release and a catchment arsenic import. [ABSTRACT FROM AUTHOR]

Based on the historical and future outputs of 17 coupled model intercomparison project phase 5 (CMIP5) models, simulation of the precipitation extremes in China was evaluated under baseline climate condition compared to a gridded daily observation dataset CN05.1. The variations in precipitation extremes for eight global warming targets were also projected. The 17 individual models and the multi-model ensemble accurately reproduced the spatial distribution of precipitation extremes, although they were limited in their ability to capture the temporal characteristics. A notable dry bias existed in Southeast China, while a wet bias was present in North and Northwest China. The precipitation extremes in China were projected to be more frequent and more intense as global temperature rise reached the 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 °C warming targets. The projected percentage changes in the annual number of days with precipitation >50 mm (R50) and total precipitation during days in which the daily precipitation exceeds the 99th percentile (R99p) are projected to increase by 25.81 and 69.14 % relative to the baseline climate for a 1.5 °C warming target, and by 95.52 and 162.00 % for a 4.0 °C warming target, respectively. As the global mean temperature rise increased from 1.5 to 5 °C, the subregions considerably affected by the East Asian summer monsoon (e.g., Southwest China, South China, and the Yangtze-Huai River Valley) were projected to experience a more dramatic increase in extreme precipitation events, in both number of days and intensity, while North and Northwest China were projected to suffer from relatively slight increases. The model uncertainties in the projected precipitation extremes in China by 17 CMIP5 models increase as global temperature rise increases. [ABSTRACT FROM AUTHOR]

Precipitation extremes play a key role in flooding risks over the Huaihe River Basin, which is important to understand their hydrological impacts. Based on observed daily precipitation and streamflow data from 1958 to 2009, eight precipitation indices and three streamflow indices were calculated for the study of hydrological impacts of precipitation extremes. The results indicate that the wet condition intensified in the summer wet season and the drought condition was getting worse in the autumn dry season in the later years of the past 50 years. The river basin had experienced higher heavy rainfall-related flooding risks in summer and more severe drought in autumn in the later of the period. The extreme precipitation events or consecutive heavy rain day events led to the substantial increases in streamflow extremes, which are the main causes of frequent floods in the Huaihe River Basin. The large inter-annual variation of precipitation anomalies in the upper and central Huaihe River Basin are the major contributor for the regional frequent floods and droughts. [ABSTRACT FROM AUTHOR]

A study of a daily precipitation database for the island of Cyprus is performed for a period of 30 years. A number of climatic indices for precipitation are calculated using the recently available CHIRPS dataset, on high spatial (0.05°) and temporal (daily) resolution. The same parameters for the same time period (1981-2010) are then calculated using the dense network of rain gauges of the Cyprus Department of Meteorology. The results show a quite promising performance regarding indices related to daily precipitation thresholds, resulting in high correlation scores. In the case of indices referring to number of days, it seems that the results are ambiguous, with medium or no correlation, probably related to the criteria used for the identification of a wet (rainy) day on the CHIRPS dataset. [ABSTRACT FROM AUTHOR]