Wet-day Frequency Research Articles

Elevation-dependent biases of raw and bias-adjusted EURO-CORDEX regional climate models in the European Alps

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate.

Dry and Wet Spells in Poland in the Period 1966–2023

The aim of this study is to present the spatial and temporal variability of the frequency of dry and wet days and dry and wet spells against the background of changes in precipitation and atmospheric circulation. The study is based on daily precipitation totals from 46 meteorological stations in Poland from 1966 to 2023. Additionally, seven circulation indices were used, namely GBI, NAO, AO, EA, EA/WR, SCAND, and AMO. Dry days are defined as days without precipitation. Wet days are days with at least 1 mm of precipitation. It was shown that dry spells are much more common than wet spells, are longer, and cover larger areas. Long-term changes in the annual characteristics of dry and wet days and spells are not statistically significant. Only the length of the most extended dry spell in the year increases. However, there are significant changes in their annual cycles. Spring is drier; in summer, precipitation decreases in the south and increases in the north; November and December, symbols of gloomy rainy weather, are increasingly drier; and rainy weather has shifted to January and February. The impact of circulation varies according to the season, with the NAO, AO, SCAND, and GBI indices having the greatest influence.

Exploring the nexus of climate variability, population dynamics, and maize production in Togo: Implications for global warming and food security

Maize, which serves as the primary staple crop in Togo, plays a crucial role in supporting rural communities and ensuring food security. Comprehending the complex relationship between Togo's maize production, population dynamics, and climate variability is crucial for mitigating the impacts of global warming and guaranteeing long-term food security. The present study examines the climate patterns and their impact on maize cultivation in Togo for the period spanning from 1990 to 2020. During this temporal interval, significant variations were seen in important climate indices. The levels of rainfall and the frequency of wet days exhibited notable fluctuations, whereas the temperature and concentrations of air pollutants, specifically carbon dioxide (CO2) and nitrous oxide (N2O), had a persistent rising trajectory. The recorded temperature exhibited an increase from 27 ​°C in 1990 to 29 ​°C in 2020. The levels of CO2 emissions had a substantial increase from 1000 ​kt in 1990 to an estimated 2500 ​kt in 2020. Similarly, the emissions of N2O witnessed a notable rise of more than 800 ​kt over the same time frame. Using time series analysis, we examined the temporal characteristics and employed ARIMA predictive models to project maize production over the upcoming decade, considering the gradual decrease in rural population density. The studies highlight the significant implications of global warming on a worldwide scale, exerting a profound impact on the Earth. The agriculture sector, specifically maize production, had notable variations in yield and overall output. Notwithstanding the inherent fluctuations in output, maize continues to maintain its position as the most widely consumed and preferred crop in Togo, even in the face of the country's rapidly expanding population. As a result, Togo has increased its imports of maize to satisfy the growing demand. The present study sheds light on the intricate relationship between production parameters, including yield and quantity, and a range of environmental variables such as the frequency of precipitation, temperature, and levels of N2O, in addition to rural and urban population density. The statement underscores the pressing necessity to confront the issue of global warming and its subsequent impacts on agricultural methodologies. Furthermore, the results underscore the significance of considering population dynamics and climate variables in the prediction of forthcoming agricultural outcomes.

Warmer and wetter: Outlining climate services for snow-dependent tourism in Norway – The case of Lofoten

Human-induced climate change potentially impacts nature-based activities in Lofoten and may limit the attractiveness of the destination for tourists seeking recreation and adventure in the mountains. As a climate service, we calculated climate indicators relevant to the tourism sector based on the representative concentration pathways RCP4.5 and RCP8.5 until 2060. We used high-resolution gridded climate data and projections to calculate indicators such as changes in the frequency and intensity of consecutive wet days, changes in precipitation type (snow, sleet, rain), changes in the number of skiing days on ungroomed, natural snow, and changes of the monthly 0 °C-isoline. We found a minor, but non-robust increase in the number of consecutive wet days with a precipitation intensity > 8 mm/day, and a clear change in the precipitation regime depending on altitude that leads to more precipitation falling as rain instead of snow. Also, a strong decrease in the number of skiing days is projected by the climate models as the monthly near-surface 0 °C-isoline increases. These are important findings for long-term planning and investments in the tourism sector in Lofoten, especially as tourism growth is considered an important tool for regional economic development. The analytical methods used in this study are transferable to analyses on a regional to national scale. National maps and data material for 11 regions were recently published on https://klimaservicesenter.no/kss/framskr/sno-sludd-regn.

Convection-Permitting Regional Climate Simulation over Bulgaria: Assessment of Precipitation Statistics

With increasing computational power, the regional climate modeling community is moving to higher resolutions of a few kilometers, named convection-permitting (CP) simulations. This study aims to present an assessment of precipitation metrics simulated with the non-hydrostatic regional climate model RegCM-4.7.1 at CP scale for a decade-long period (2001–2010) for Bulgaria. The regional climate simulation at 15 km grid spacing uses ERA-Interim (0.75° × 0.75°) re-analysis as the driving data and parametrized deep convection. The kilometer-scale simulation at 3 km horizontal grid spacing is nested into regional climate simulation using parametrized shallow convection only. The CP simulation is evaluated against daily and hourly datasets available for the selected period and is compared with the coarser resolution driving simulation. The results show that the model represents well the spatial distribution of mean precipitation at the regional and kilometer scale for the territory of Bulgaria. However, the CP_RegCM_3km model produces too much precipitation over the mountains and shows the largest biases in the summer season (above 100%). At the daily scale, improvements are found in CP simulation for precipitation wet-day intensity and extreme precipitation in the autumn and for wet-day frequency in the summer. At the hourly scale, the kilometer-scale simulation improved the performance of wet-hour precipitation intensity in all seasons compared with coarse-resolution simulation (−23% vs. −46% in MAM; −10% vs. −37% in JJA; −47% vs. −53% in SON; −54% vs. −62% in DJF) and extreme precipitation in the autumn (−7% vs. −51%) and winter (−34% vs. −58%). The representation of wet-hour frequency was improved by CP_RegCM_3km in all seasons, except summer (−3.1% vs. −6.7% in spring; 0.5% vs. −3.8% in autumn and −7.7% vs. −11.5% in winter).

Evaluating the Impact of Statistical Bias Correction on Climate Change Signal and Extreme Indices in the Jemma Sub-Basin of Blue Nile Basin

This study evaluates the effect of the statistical bias correction techniques of distribution mapping and linear scaling on climate change signals and extreme rainfall indices under different climate change scenarios in the Jemma sub-basin of the Upper Blue Nile Basin. The mean, cumulative distribution function (CDF), mean absolute error (MAE), probability of wet days (Prwet (%)), and 90th percentile (X90 (mm)) of observed rainfall and the regional climate model (RCM) simulations of rainfall with and without statistical bias correction were compared with the historical climate (1981–2005). For future (2071–2100) climate scenarios, the change in climate signal and extreme rainfall indices in the RCM simulations with and without bias correction were also evaluated using different statistical metrics. The result showed that the statistical bias correction techniques effectively adjusted the mean annual and monthly RCM simulations of rainfall to the observed rainfall. However, distribution mapping is effective and better than linear scaling for adjusting the probability of wet days and the 90th percentile of RCM simulations. In future climate scenarios, RCM simulations showed an increase in rainfall. However, the statistically bias-adjusted RCM outputs revealed a decrease in rainfall, which indicated that the statistical bias correction techniques triggered a change in climate signal. Statistical bias correction methods also result in changes in the extreme rainfall indices, such as frequency of wet days (R1mm), number of heavy precipitation days (R10mm), number of very heavy rainfall days (R20mm), and other intensity and frequency indices.

Changes in mean and extreme homogeneous precipitation in China during 1960–2020

This study aims to explore the observed national and regional changes in the seasonal mean and extreme precipitation in mainland China between 1960 and 2020 using data from 678 stations. Tests of homogeneity (e.g. standard normal homogeneity test, Pettitt's test and Buishand range test) are applied to identify homogeneous stations for the trend analysis. The non-parametric Mann-Kendall test and a modified Theil-Sen slope estimator were then used to examine changes in the precipitation characteristics. Our results show that despite a decreasing trend in the frequency of wet days, the mean precipitation has actually increased. Extreme precipitation has also increased, and in many locations, at greater rates than those of the mean. This is principally due to a greater rate of increase per degree of warming, of 8% for extreme precipitation compared to 7% for the mean. However, considerably regional and seasonal variability has been seen in the trends. Mean and extreme precipitation trends are positive throughout in western China for all seasons, but they vary in eastern China. Mean precipitation increased slightly in the southeast but decreased in the northeast of the country. Extreme precipitation increased in both regions, but the increases were greater in the southeast. Trends, additionally, also differed within regions, especially over eastern China. In southeast China, precipitation increased in the east and decreased in the west. In northeast China, precipitation increased in the north and decreased in the south. Using a multiple regression model, we found that changes during summer precipitation clearly dominate the overall annual changes in most regions. Most long-term changes over eastern China appear to be linked to those of the East-Asian and South-Asian Summer Monsoons.

Positive correlation between wet-day frequency and intensity linked to universal precipitation drivers

Understanding precipitation is essential for quantifying weather and climate-related risks. Changes in precipitation climatology are typically based on independent analysis of precipitation frequency and intensity. Here we show that where it rains more often, it also rains harder. When grouping global precipitation reanalysis data and observations from the past 40 years into regions of similar wet-day frequency, regardless of geographical separation, there is a strong correlation with wet-day intensity distributions. These wet-day-frequency regions are also more physically coherent than regions based on geographical location. We find the coherent relationship between wet-day frequency and intensity distributions is partially explained by wet-day-frequency regions having similar vertical velocity and convective available potential energy distributions, once polar regions are excluded. These represent dynamic and thermodynamic processes that indicate how conducive wet-day-frequency regions are to large-scale and convective precipitation. This suggests that the main drivers of precipitation are universal. We also show that extreme-precipitation metrics are dependent on wet-day frequency within our framework. Our results imply that wet-day frequency could be used to derive estimates of extreme-precipitation climate indices and corresponding uncertainties, these uncertainties being related to local processes.

Impacts of a weakened AMOC on precipitation over the Euro-Atlantic region in the EC-Earth3 climate model

Given paleoclimatic evidence that the Atlantic Meridional Overturning Circulation (AMOC) may affect the global climate system, we conduct model experiments with EC-Earth3, a state-of-the-art GCM, to specifically investigate, for the first time, mechanisms of precipitation change over the Euro-Atlantic sector induced by a weakened AMOC. We artificially weaken the strength of the AMOC in the model through the release of a freshwater anomaly into the Northern Hemisphere high latitude ocean, thereby obtaining a ~ 57% weaker AMOC with respect to its preindustrial strength for 60 model years. Similar to prior studies, we find that Northern Hemisphere precipitation decreases in response to a weakened AMOC. However, we also find that the frequency of wet days increases in some regions. By computing the atmospheric moisture budget, we find that intensified but drier storms cause less precipitation over land. Nevertheless, changes in the jet stream tend to enhance precipitation over northwestern Europe. We further investigate the association of precipitation anomalies with large-scale atmospheric circulations by computing weather regimes through clustering of geopotential height daily anomalies. We find an increase in the frequency of the positive phase of the North Atlantic Oscillation (NAO+), which is associated with an increase in the occurrence of wet days over northern Europe and drier conditions over southern Europe. Since a ~ 57% reduction in the AMOC strength is within the inter-model range of projected AMOC declines by the end of the twenty-first century, our results have implications for understanding the role of AMOC in future hydrological changes.

Analyzing future rainfall variations over southern malay peninsula based on CORDEX-SEA dataset

Gridded rainfall datasets based on various data sources and techniques have emerged to help describe the spatiotemporal features of rainfall patterns over large areas and have gained popularity in many regional/global climatic analyses. This study explored future variations of rainfall characteristics over peninsula Malaysia and Singapore region based on rainfall indices of PRCPTOT, Rx1day, Rx5day, R95pTOT, R1mm, and R20mm, under 9 CORDEX-SEA RCM datasets with RCP8.5 emission scenario. A monthly quantile delta mapping method (MQDM) was adopted for bias-correction of the RCM modelled data. It was indicated that all the studied rainfall indices have long-term variations both temporally and spatially. Generally, the further the future, the higher the variability and uncertainty of indices. For the study region, the relative increments of the medians from RCM models averaged over all climatic zones in the far future are 40.3%, 25.9%, and 4.7% for Rx1day, Rx5day and R95pTOT, respectively. The annual rainfall amount (PRCPTOT) in the long run would likely increase mainly in the northeast coastal zone and drop in most of other areas over the peninsula, with the median being -5.9% averaged over all zones. The frequency of wet days (R1mm) would generally drop over the whole peninsula, with the median averaged over all zones being -6.8% in the far future. The frequency of heavy rains (R20mm) would overall decrease (by -3.4% in average in the far future) but might still notably increase in the northeast zone (NE) at both annual and southwest monsoon. The extreme conditions implied from individual RCM models could be more alarming. The study result could be useful in revealing the essential spatiotemporal variations of rainfall over the peninsula from short- to long-term futures and supporting large-scale flood risk assessment and adaptation planning.

Evaluation of the Spatiotemporal Distribution of Precipitation Using 28 Precipitation Indices and 4 IMERG Datasets over Nepal

Accurate accounting of spatiotemporal variability of precipitation is essential for understanding the changing climate. Among the available precipitation estimates, the Global Precipitation Measurement (GPM) is an international satellite network providing advanced global precipitation estimates. The integrated multi-satellite retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation and yields a better performance in detecting precipitation events and spatial resolution. Here, we used twenty years (2001–2020) of IMERG Final data over the entire Nepal to analyze the spatial and temporal distribution of precipitation. This study evaluates the dynamic characteristics of the precipitation amounts, intensities, frequencies, and other relevant data across Nepal, using four IMERG datasets: (i) microwave only, (ii) infrared only, (iii) multi satellites gauge uncalibrated, and (iv) multi satellites gauge calibrated. A total of 28 precipitation indices was computed: threshold-based counts, consecutive days, precipitation amounts and extremes, precipitation intensity, percentile-based extremities, proportion-based indices, and additional seasonal indices. Results show that all four IMERG datasets are promising in capturing spatial details. The frequency of wet days corresponds with ground-based precipitation. Still, most indices, including consecutive wet days, annual and monsoon precipitation, and days when precipitation equaled or exceeded 20 and 50 mm, were substantially underestimated. In addition, the microwave-only dataset highly underestimated the precipitation amount. Notably, a substantial proportion of false alarms is a problem for all four IMERG datasets. Moreover, our results demonstrate that the IMERG uncalibrated dataset tends to overestimate precipitation during heavy precipitation events. These advantages and shortcomings of IMERG datasets over the rugged terrain of Nepal can provide useful feedback for sensor and algorithm developers to overcome limitations and improve retrieval algorithms. The study findings are helpful to the broader data users and practitioners for effective water decision applications.

The effects of weather on beef carcass and growth traits

To predict the impact of climate change on our beef animals and systems, we need a better understanding of how beef cattle traits are affected by varying weather and frequency of extreme events. We analysed the effect of minimum and maximum temperatures and average daily precipitation on a range of important carcass traits, including age at slaughter, cold carcass weight, carcass growth rate and conformation and fat score (N = >1.6 million), as well as calf 200-day live weight and growth rate (N = >270 000), using data from abattoirs across Britain (carcass traits) and calves in Scottish suckler beef herds (live weights and growth). Animals which experienced higher daily maximum and minimum temperatures had slower carcass and calf growth rates. Increased precipitation also led to poorer cold carcass weights, conformation scores, calf 200-day weights and calf growth. We also analysed the effect of frequency of extreme weather events, including heatwaves, cold waves, and dry and wet days. The frequency of heatwaves, dry and wet days were shown to have significant negative effects on almost all traits considered, for example, predicting that an increase in the frequency of heatwaves by 1 day per 100 days of life would reduce cold carcass weights by about 200 g and increase age at slaughter by about 3 days. Results show that varying weather and frequency of extreme weather, across the lifetime of a beef animal, influences traits which affect the potential profit for a beef farmer. These effects may be due to several factors, including direct effects on the animal, as well as feed availability and management decisions made by the farmer. However, there is potential to mitigate negative effects through a range of animal management strategies.

Spatiotemporal Variation of Actual Evapotranspiration and Its Relationship with Precipitation in Northern China under Global Warming

The analysis of actual evapotranspiration (ETa) changes is of great significance for the utilization and allocation of water resources. In this study, ETa variability in northern China (aridity index < 0.65) is investigated based on the average of seven datasets (GLEAM, GLASS, a complementary relationship-based dataset, CRA-40, MERRA2, JRA-55, and ERA5-Land). The results show that ETa increases significantly from 1982 to 2017. Limited by water supply, ETa is significantly correlated with precipitation (R = 0.682), whereas the increase in precipitation is insignificant (p = 0.151). Spatially, the long-term trend of ETa is also not completely consistent with that of precipitation. According to a singular value decomposition (SVD) analysis, the trend of ETa is mainly related to the first four leading SVD modes. Homogeneous correlation patterns indicate that more precipitation generally leads to high ETa; however, this relationship is modulated by other factors. Overall, positive potential evapotranspiration anomalies convert more surface water into ETa, resulting in a higher increase in ETa than in precipitation. Specifically, ETa in the northern Tibetan Plateau is associated with meltwater generated by rising temperatures, and ETa in the Badain Jaran Desert is highly dependent on the wet-day frequency. Under global warming, the inconsistency between ETa and precipitation changes has a great impact on water resources in northern China.

Seasonal prediction of the Caribbean rainfall cycle

Rainfall in the Caribbean is an important resource for numerous stakeholders in the region. Based on previous work, which identified several variables that could provide predictive skill of rainfall in the region, canonical correlation analysis is applied to assess forecast skill for station-averaged sub-regional frequency and intensity of the Early-Rainy Season (ERS) and Late-Rainy Season (LRS) wet days, and magnitude of the Mid-Summer Drought (MSD). Predictor fields are explored from the ERA-Interim and North American Multi-Model Ensemble (NMME). The use of sea-level pressure (SLP), 850 hPa zonal winds (u850), vertically integrated zonal (UQ), and meridional (VQ) moisture fluxes show comparable, if not better, forecast skill than sea-surface temperatures (SSTs), which is generally the commonly-used predictor in a given region’s seasonal climate forecasts. Generally, the highest predictive skill is found for the frequency of wet days. Rainfall characteristics in the Central and Eastern Caribbean have significant predictive skill. Forecast skill of rainfall characteristics in the Northwestern and Western Caribbean are lower and less consistent. The sub-regional differences and consistently significant skill across lead times up to at least two months can be attributed to persistent SST/SLP anomalies during the ERS that resemble the North Atlantic Oscillation, and that resembles the summer-time onset of the El Niño-Southern Oscillation during the LRS. The anomalous spatial patterns during the MSD bear resemblance to both the ERS and LRS signals. The results provide additional variables that can be used to forecast rainfall characteristics in the Caribbean and a way to tailor seasonal forecasts for each sub-region.

Robust bias-correction of precipitation extremes using a novel hybrid empirical quantile-mapping method

High-resolution, daily precipitation climate products that realistically represent extremes are critical for evaluating local-scale climate impacts. A popular bias-correction method, empirical quantile mapping (EQM), can generally correct distributional discrepancies between simulated climate variables and observed data but can be highly sensitive to the choice of calibration period and is prone to overfitting. In this study, we propose a hybrid bias-correction method for precipitation, EQM-LIN, which combines the efficacy of EQM for correcting lower quantiles, with a robust linear correction for upper quantiles. We apply both EQM and EQM-LIN to historical daily precipitation data simulated by a regional climate model over a region in the northeastern USA. We validate our results using a five-fold cross-validation and quantify performance of EQM and EQM-LIN using skill score metrics and several climatological indices. As part of a high-resolution downscaling and bias-correction workflow, EQM-LIN significantly outperforms EQM in reducing mean, and especially extreme, daily distributional biases present in raw model output. EQM-LIN performed as good or better than EQM in terms of bias-correcting standard climatological indices (e.g., total annual rainfall, frequency of wet days, total annual extreme rainfall). In addition, our study shows that EQM-LIN is particularly resistant to overfitting at extreme tails and is much less sensitive to calibration data, both of which can reduce the uncertainty of bias-correction at extremes.

21st Century alpine climate change

A comprehensive assessment of twenty-first century climate change in the European Alps is presented. The analysis is based on the EURO-CORDEX regional climate model ensemble available at two grid spacings (12.5 and 50 km) and for three different greenhouse gas emission scenarios (RCPs 2.6, 4.5 and 8.5). The core simulation ensemble has been subject to a dedicated evaluation exercise carried out in the frame of the CH2018 Climate Scenarios for Switzerland. Results reveal that the entire Alpine region will face a warmer climate in the course of the twenty-first century for all emission scenarios considered. Strongest warming is projected for the summer season, for regions south of the main Alpine ridge and for the high-end RCP 8.5 scenario. Depending on the season, medium to high elevations might experience an amplified warming. Model uncertainty can be considerable, but the major warming patterns are consistent across the ensemble. For precipitation, a seasonal shift of precipitation amounts from summer to winter over most parts of the domain is projected. However, model uncertainty is high and individual simulations can show change signals of opposite sign. Daily precipitation intensity is projected to increase in all seasons and all sub-domains, while the wet-day frequency will decrease in the summer season. The projected temperature change in summer is negatively correlated with the precipitation change, i.e. simulations and/or regions with a strong seasonal mean warming typically show a stronger precipitation decrease. By contrast, a positive correlation between temperature change and precipitation change is found for winter. Among other indicators, snow cover will be strongly affected by the projected climatic changes and will be subject to a widespread decrease except for very high elevation settings. In general and for all indicators, the magnitude of the change signals increases with the assumed greenhouse gas forcing, i.e., is smallest for RCP 2.6 and largest for RCP 8.5 with RCP 4.5 being located in between. These results largely agree with previous works based on older generations of RCM ensembles but, due to the comparatively large ensemble size and the high spatial resolution, allow for a more decent assessment of inherent projection uncertainties and of spatial details of future Alpine climate change.

Predictive Modelling of Statistical Downscaling Based on Hybrid Machine Learning Model for Daily Rainfall in East-Coast Peninsular Malaysia

In recent years, climate change has demonstrated the volatility of unexpected events such as typhoons, flooding, and tsunamis that affect people, ecosystems and economies. As a result, the importance of predicting future climate has become even direr. The statistical downscaling approach was introduced as a solution to provide high-resolution climate projections. An effective statistical downscaling scheme aimed to be developed in this study is a two-phase machine learning technique for daily rainfall projection in the east coast of Peninsular Malaysia. The proposed approaches will counter the emerging issues. First, Principal Component Analysis (PCA) based on a symmetric correlation matrix is applied in order to rectify the issue of selecting predictors for a two-phase supervised model and help reduce the dimension of the supervised model. Secondly, two-phase machine learning techniques are introduced with a predictor selection mechanism. The first phase is a classification using Support Vector Classification (SVC) that determines dry and wet days. Subsequently, regression estimates the amount of rainfall based on the frequency of wet days using Support Vector Regression (SVR), Artificial Neural Networks (ANNs) and Relevant Vector Machines (RVMs). The comparison between hybridization models’ outcomes reveals that the hybrid of SVC and RVM reproduces the most reasonable daily rainfall prediction and considers high-precipitation extremes. The hybridization model indicates an improvement in predicting climate change predictions by establishing a relationship between the predictand and predictors.

Changes in extreme precipitation events in the Zambezi River basins based on CORDEX‐CORE models: Part I—Evaluation of historical simulation

AbstractThis study evaluates the ability of the Coordinated Output for Regional Evaluations (CORE) initiated under the Coordinated Regional Climate Downscaling Experiment (CORDEX) to reproduce austral summer extreme precipitation indices over the Zambezi River basin (ZRB) from 1983 to 2005. The ability of the RCMs to simulate the spatial distributions of observed extreme precipitation was assessed using multiple datasets. The spatially averaged performance of the CORDEX‐CORE ensemble mean (CORE‐ENS) and RCM ensembles (RCM‐ENSEMBLES developed from different combinations of CORE‐ENS ensemble members) was evaluated using statistical metrics. The results showed similarities in the spatial distribution of extreme precipitation among the observations. However, there were considerable differences in the magnitude of extreme precipitation among the observations. The frequency of days with very heavy precipitation (R20) showed the largest differences in magnitude among the observations. Although there are differences in magnitude, CORE‐ENS, its ensemble members, and RCM‐ENSEMBLES can capture the spatial distribution of extreme precipitation. Specifically, CORE‐ENS, its ensemble members, and RCM‐ENSEMBLES overestimated (underestimated) the frequency of rainy days (RR1) and maximum consecutive wet days (CWD) (maximum consecutive dry days [CDD]). In contrast, some members of CORE‐ENS overestimate, while others underestimate, the simple daily intensity index (SDII), the frequency of days with heavy precipitation (R10), and R20, depending on the choice of reference data. The Consortium for Small‐scale MOdelling in CLimate Mode (CCLM) and Regional MOdel (REMO) performed better than the Regional Climate Model (RegCM4.7) in simulating the spatial distributions of extreme precipitation. The regionally averaged uncertainties (biases) of the CORE‐ENS, its ensemble members, and RCMENSEMBLES were within the range of those in the observational datasets, except for CWD (CWD and RR1). Overall, CORE‐ENS performed better than its ensemble members in simulating extreme precipitation.

The evolving roles of intensity and wet season timing in rainfall regimes surrounding the Red Sea

The Red Sea is surrounded by a diverse mixture of climates and is spanned by opposite hydrologic end-uses and geopolitical states. Unique water supply management challenges on both sides (related to agricultural and trans-boundary conflict in East Africa, and to groundwater depletion in the Arabian Peninsula) are made more severe by a rising demand, which underscores the importance of understanding shifts in rainfall supply to aid effective action. In this study, we characterize the relative importance of rainfall intensities to annual rainfall, the onset and duration of wet seasons, and the (statistically significant) trends in each of these over the region from 1981 through 2020 using daily gridded (0.05°) precipitation estimates. Results show that heavy rainfall (above 20 mm d−1) does not necessarily benefit annual totals, as the wettest regions are shaped by moderate (between 5 and 20 mm d−1) rainfall coupled with prolonged wet seasons. Observed trends in annual rainfall are underlain by interactions between shifting wet season lengths and rainfall intensities. Wet season length increases for 26% of the region, dampening the inherent drying resulting from shifts toward less-intense rainfall, and bolstering the inherent wetting from shifts toward more-intense rainfall. Regions shifting toward less- (more-)intense rainfall without an expanding wet season generally show negative (insignificant) rainfall trends. This reveals an important control that wet-day frequency has over wet-day intensity alone in shaping annual rainfall changes. We emphasize that the large-scale distribution of these shifts and their regional importance should punctuate cooperative efforts in sustainable resource management and transboundary governance.

Reanalysis‐driven multi‐RCM high‐resolution simulation of precipitation within CORDEX East Asia Phase II

AbstractUnder the Coordinated Regional Downscaling Experiment‐East Asia second phase (CORDEX‐EA‐II) framework, the performances of three regional climate models (RCMs), including Weather Research and Forecasting (WRF) model, RegCM4 and Consortium for Small‐Scale Modelling model in Climate Mode (CCLM), in simulating the climatic distribution, diurnal variation and extremes of precipitation during 1989–2009 are evaluated. The RCMs are driven by the ERA‐Interim reanalysis and the evaluation is based on both the gridded gauge‐based and the satellite‐derived precipitation data. In general, the RCMs can reproduce the spatial pattern of the mean daily precipitation, with the spatial correlations (SCORs) larger than 0.66, and CCLM has the lowest root‐mean‐square error over East Asia. For the diurnal cycle of precipitation, WRF outperforms the other two RCMs despite the peak time about 3 hr ahead in some areas. RegCM4 overestimates the amplitude of the diurnal curve, and CCLM has limited ability to simulate the diurnal variation over most areas. All the three RCMs can reproduce the spatial patterns of the extreme indices, with the SCORs larger than 0.57. WRF and CCLM perform the best for the frequency of wet days (R1mm) and moderate rain (R20mm), respectively. The intensity indices are overestimated over most land areas, with relatively higher SCORs in WRF and relatively lower biases in RegCM4. The consecutive wet days (CWD) can be well simulated by WRF and CCLM; however, RegCM4 overestimates it over most regions in East Asia. The overestimation of the extreme intensity and frequency indices may be related to the higher water vapour content and the weaker water vapour divergence in the lower layer, as well as the higher mid‐layer geopotential height, especially in CCLM.