Number Of Heavy Precipitation Days Research Articles

Projected Changes in Extreme Precipitation Patterns across Algerian Sub-Regions

Extreme precipitation events play a crucial role in shaping the vulnerability of regions like Algeria to the impacts of climate change. To delve deeper into this critical aspect, this study investigates the changing patterns of extreme precipitation across five sub-regions of Algeria using data from 33 model simulations provided by the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6). Our analysis reveals a projected decline in annual precipitation for four of these regions, contrasting with an expected increase in desert areas where annual precipitation levels remain low, typically not exceeding 120 mm. Furthermore, key precipitation indices such as maximum 1-day precipitation (Rx1day) and extremely wet-day precipitation (R99p) consistently show upward trends across all zones, under both SSP245 and SSP585 scenarios. However, the number of heavy precipitation days (R20mm) demonstrates varied trends among zones, exhibiting stable fluctuations. These findings provide valuable foresight into future precipitation patterns, offering essential insights for policymakers and stakeholders. By anticipating these changes, adaptive strategies can be devised to mitigate potential climate change impacts on crucial sectors such as agriculture, flooding, water resources, and drought.

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

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

Evaluation of future climatology and its uncertainty under SSP scenarios based on a bias processing procedure: A case study of the Lancang-Mekong River Basin

The Lancang-Mekong River is the most important international river in Southeast Asia, and its water resources are invaluable for the survival of the inhabitants of the basin, while the climatic conditions have a significant impact on the production and livelihoods of the riparian countries. In this study, future climate change in the Lancang-Mekong River Basin (LMRB) was projected based on precipitation and temperature from CMIP6. First, a bias processing procedure was established to correct the initial bias of climate models using the Daily Bias Correction (DBC) method, and the performance of equal weighted averaging (EW) and Bayesian mode averaging (BMA) of multi-model ensembles was compared. The characteristics of future precipitation and temperature changes were projected in terms of mean climatology, seasonal variation and extreme events, respectively, from which the future trends of the four weather extremes (dry-cold, dry-hot, wet-cold and wet-hot) were projected. Evaluation based on mean climatology, daily frequency distribution, temporal change and its bias showed that the climate bias correction procedure has good performance. It was found that future precipitation in the LMRB will increase by 50–120 mm in the near future (2031–2050) and 150–400 mm in the far future (2061–2090), with higher increases in the upper Mekong River Basin (MRB), Lower MRB and Mekong Delta than in the Lancang River Basin (LRB) and middle MRB. Future increase in precipitation will occur mainly from June to October, while April and May will see a decrease by 10–30 mm, and the number of heavy precipitation days (R25, daily precipitation exceeding 25 mm) will increase by about 5 days in the eastern part of the lower and middle MRB. The mean temperature will increase by 1–3 °C in most parts of the LMRB, with the LRB experiencing an increase of about 3 °C higher than in the MRB. Future maximum and minimum daily temperatures will increase by 1–2 °C in the near future and by 2–5 °C in the far future. Dry-hot days will increase by about 20 days in the lower and middle MRB, and wet-hot days will increase significantly in the MRB, by 30–80 days in the near future and by 50–100 days in the far future.

Extreme weather events and its impacts on rice production in coastal Odisha region of India

The study examines extreme daily precipitation and temperature trends in coastal Odisha, India by calculating 18 weather indices (8 temperature indices and 10 rainfall indices) using the RClimDex software package for the period 1980-2010. Statistical significance of the indices was determined through trend analysis using linear regression and non-parametric Mann-Kendall test. Results indicated, a strong and significant trend in temperature indices while the weak and non-significant trend in precipitation indices. The positive trend in Tmax mean, Tmin mean, TN90p (warm nights), TX90p (warm days), diurnal temperature range, warm spell duration indicator, consecutive dry days indicates increasing the frequency of warming events in coastal Odisha. Similarly, positive trend in highest maximum 1-day precipitation, highest maximum 2-consecutive day precipitation, highest maximum 3 consecutive day precipitation, highest maximum 5-consecutive day precipitation, number of heavy precipitation days (64.5mm), number of very heavy precipitation days (124.5mm) and negative trend in the number of rainy days (R2.5mm), consecutive wet days indicate changes toward the more intense and poor distribution of precipitation in coastal Odisha. Extreme precipitation and temperature events negatively impacted rice yield, with a sharp decline observed in all coastal districts. The study highlights the need for new technology/management practices to minimize these impacts.

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.

Hysteresis and irreversibility of global extreme precipitation to anthropogenic CO2 emission

Understanding of extreme precipitation change in response to CO2 forcing and associated socioeconomic exposure is limited. In this study, a comprehensive analysis is conducted to explore the response of global extreme precipitation to CO2 forcing in terms of hysteresis and reversibility effect and associated population exposure. In this regard, climate outputs under two idealized CO2 scenarios such as ramp-up (RU; about +1% annually until quadrupling of present level) and ramp-down (RD; around −1% annually set back to present level) from Community Earth System Model version 1.2, and the projected population data from the five shared Socioeconomic Pathways (SSPs) are used. Extreme precipitation events are evaluated using the number of heavy precipitation days (R30 mm), maximum consecutive 5-day precipitation (Rx5day), and the precipitation of very wet days (R95pTOT) indices. Results show that the magnitude of extreme precipitation change and associated population exposure is higher in the CO2 reduction period (RD) than in RU. All the indices show substantial irreversible and hysteresis effects, ∼69% of the global land is expected to experience irreversible changes in extreme precipitation. Further, the hotspots of irreversibility (the region with irreversible change and a large hysteresis) will emerge in >20% of the global area. Spatially, strong hysteresis and irreversibility are particularly concentrated over global land monsoon regions. The leading exposure is estimated under SSP3 combined with both RU and RD periods. Under the SSP3-RD combination, the highest population exposure is estimated at ∼67.1% (globally averaged), and ∼72% (averaged over hotspots) higher than that of the present day. The exposed population is prominent in South Africa and Asia. Notably, the population change effect is the principal factor in global exposure change, while it is the climate change effect over the hotspots of irreversibility. These findings provide new insight into policymaking that only CO2 mitigation effort is not enough to cope with extreme precipitation, rather advanced adaptation planning is a must to have more socio-economic benefits.

Influence of Precipitation Effects Induced by Large-Scale Irrigation in Northwest China on Soil Erosion in the Yellow River Basin

Large-scale irrigation can alter the regional water cycle process, which changes the structure and spatiotemporal distribution of local and downwind precipitation, impacting soil erosion in both the irrigated areas and the surrounding regions. However, the effects of large-scale irrigation on soil erosion in downwind vulnerable areas have not been investigated. The study used the high-resolution regional climate model (RegCM4) and the revised universal soil loss equation (RUSLE) to examine the effects of irrigation-induced precipitation in Northwest China on the frequency, distribution, and intensity of precipitation in the Yellow River Basin (YRB) under different Representative Concentration Pathways (RCPs). The response characteristics of soil erosion to the irrigation-induced precipitation effects and its relationship with slope, elevation, and land use type were analyzed as well. The results indicate that soil erosion in most regions of the YRB is below moderate, covering 84.57% of the basin. Irrigation leads to a 10% increase in summer precipitation indices (e.g., total wet-day precipitation, consecutive wet days, number of wet days with precipitation ≥ 1 mm, and number of heavy precipitation days with precipitation ≥ 12 mm) in the northwest of the basin. Irrigation also leads to a change in local circulation, resulting in reduced precipitation in the southeast of the basin, particularly under the RCP8.5 scenario. The transformation of erosion intensity between low-grade and high-grade erosion is relatively stable and small under the influence of precipitation. However, soil erosion changes display strong spatial heterogeneity, inter-annual and intra-annual fluctuations, and uncertainties. The findings of this study can be helpful for policymakers and water resource managers to better understand the impacts of large-scale irrigation on the environment and to develop sustainable water management strategies.

Assessment of Climate Indices over the Carpathian Basin Based on ALADIN5.2 and REMO2015 Regional Climate Model Simulations

The Hungarian Meteorological Service has been conducting climate model simulations in order to assess the effects of climate change in the Carpathian Basin and provide data for impact research and stakeholders. Two regional climate models are used: ALADIN-Climate 5.2 (hereafter ALADIN5.2) and REMO2015. They were tested for the past when the lateral boundary conditions were taken from two sources. ERA-Interim reanalysis was used in the evaluation experiment, while the CNRM-CM5 and the MPI-ESM-LR global climate models (GCM) provided the forcings in the control experiments. The model outputs were compared with the CarpatClim-HU observational dataset for the 1981–2000 period. Future projections were carried out with the RCP4.5 and RCP8.5 scenarios, and the results were analyzed for 2021–2050 and 2071–2100. The evaluation of the results focused mainly on climate indices calculated from temperature and precipitation. The validation results showed that REMO2015 assessed the mean temperature well, but the indices based on the minimum and maximum temperature had a significant bias which has to be taken into account when interpreting future changes. The model overestimated the minimum temperature in summer, which might affect the number of tropical nights. Moreover, the maximum temperature was underestimated; thus, the derived indices, such as the occurrence of summer days and hot days, were profoundly underestimated. In comparison, ALADIN5.2 had smaller biases for the high temperature indices; moreover, the number of hot days and extremely cold days was overestimated. Taking future projections into account, we can clearly see that the results of REMO2015 show a much more moderate increase in temperature than ALADIN5.2. The reasons are yet unknown and require further investigation. In spring and summer, the number of wet days was slightly overestimated, while the number of heavy precipitation days was marginally underestimated. The projections showed the highest uncertainty in the changes in mean summer precipitation and other precipitation indices. Although the REMO2015 model assessed a decrease in precipitation, ALADIN5.2 projected an increase in precipitation with a similar magnitude.

Impacts of reductions in non-methane short-lived climate forcers on future climate extremes and the resulting population exposure risks in eastern and southern Asia

Abstract. Non-methane short-lived climate forcers (SLCFs), including aerosols, ozone, and their precursors, are important climate forcings and primary air pollutants. Future stringent SLCF emissions controls to mitigate air pollution will substantially impact regional climate change. Here, we investigate the changes in future climate extremes and resulting population exposure risks in Asia during 2031–2050 in response to non-methane SLCF emissions reductions using multi-model ensemble (MME) simulations under two shared socioeconomic pathway (SSP) scenarios with the same greenhouse gas (GHG) emissions but weak (SSP3-7.0) and strong (SSP3-7.0-lowNTCF) air quality control measures from the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP), which is endorsed by the Coupled Model Intercomparison Project phase 6 (CMIP6). The MME results show that future reductions in non-methane SLCF emissions lead to an increase of 0.23 ± 0.16 W m−2 in global annual mean effective radiative forcing, thereby magnifying the GHG-induced global surface warming by 0.19 ± 0.1 K during 2031–2050. In terms of the entire study area of Asia, the additional warming caused by the non-methane SLCF reductions increases the temperature of the hottest days (TXx) by 0.3 ± 0.1 K, the percentage of warm days (TX90p) by 4.8 ± 2.2 %, the number of tropical nights (TR) by 1.7 ± 0.8 d, the warm-spell duration (WSDI) by 1.0 ± 0.4 d, the number of heavy precipitation days (R10) by 1.0 ± 0.5 d, the maximum consecutive 5 d precipitation (RX5day) by 1.0 ± 0.3 mm, and the total wet-day precipitation (R95p) by 16.4 ± 7.3 mm during 2031–2050. In terms of sub-regional changes in temperature extremes, the largest regional increases in TXx, TX90p, and WSDI occur in northern India (NIN) and northern China (NC). Relatively large increases in TR are projected in NC and the Sichuan Basin (SCB), reaching 5.1 ± 2.5 d and 4.9 ± 3.3 d, respectively. For precipitation extremes, the regional changes are greatest in southern China (SC), particularly southwestern China (SWC), where reductions in non-methane SLCF emissions increase R10 by 2.5 ± 1.9 d, RX5day by 2.5 ± 1.5 mm, and R95p by 37.5 ± 22.6 mm. Moreover, the populations exposed to temperature and precipitation extremes increase most sharply in NIN, reaching (32.2 ± 11.4) × 107 person-days and (4.6 ± 6.1) × 106 person-days during 2031–2050, respectively, followed by NC and SCB. Our results highlight the significant impacts of non-methane SLCF reductions on future climate extremes and related exposure risks in eastern and southern Asia, which are comparable to the impact associated with increased GHG forcing in some regions.

Climate change and spatio-temporal trend analysis of climate extremes in the homogeneous climatic zones of Pakistan during 1962-2019.

Climate extremes, such as heat waves, droughts, extreme rainfall can lead to harvest failures, flooding and consequently threaten the food security worldwide. Improving our understanding about climate extremes can mitigate the worst impacts of climate change and extremes. The objective here is to investigate the changes in climate and climate extremes by considering two time slices (i.e., 1962–1990 and 1991–2019) in all climate zones of Pakistan by utilizing observed data from 54 meteorological stations. Different statistical methods and techniques were applied on observed station data to assess changes in temperature, precipitation and spatio-temporal trends of climatic extremes over Pakistan from 1962 to 2019. The Mann-Kendal test demonstrated increasing precipitation (DJF) and decreasing maximum and minimum temperatures (JJA) at the meteorological stations located in the Karakoram region during 1962–1990. The decadal analysis, on the other hand, showed a decrease in precipitation during 1991–2019 and an increase in temperature (maximum and minimum) during 2010–2019, which is consistent with the recently observed slight mass loss of glaciers related to the Karakoram Anomaly. These changes are highly significant at 5% level of significance at most of the stations. In case of temperature extremes, summer days (SU25) increased except in zone 4, TX10p (cold days) decreased across the country during 1962–1990, except for zones 1 and 2. TX90p (warm days) increased between 1991–2019, with the exception of zone 5, and decreased during 1962–1990, with the exception of zones 2 and 5. The spatio-temporal trend of consecutive dry days (CDD) indicated a rising tendency from 1991 to 2019, with the exception of zone 4, which showed a decreasing trend. PRCPTOT (annual total wet-day precipitation), R10 (number of heavy precipitation days), R20 (number of very heavy precipitation days), and R25mm (very heavy precipitation days) increased (decreased) considerably in the North Pakistan during 1962–1990 (1991–2019). The findings of this study can help to address some of the sustainable development goals related climate action, hunger and environment. In addition, the findings can help in developing sustainable adaptation and mitigation strategies against climate change and extremes. As the climate and extremes conditions are not the uniform in all climate zone, therefore, it is suggested to the formers and agriculture department to harvest crops resilient to the climatic condition of each zone. Temperature has increasing trend in the northern Pakistan, therefore, the concerned stakeholders need to make rational plans for higher river flow/flood situation due to snow and glacier melt.

Spatiotemporal Variability in Precipitation Extremes in the Jianghuai Region of China and the Analysis of Its Circulation Features

In the context of global warming, changes in extreme-precipitation events are becoming increasingly complex, and investigating the spatial and temporal variation characteristics of extreme precipitation is extremely important for scientific water-resource planning, preventing new climate risks and maintaining ecosystem balances. Based on the daily precipitation from 1960–2017 at 15 meteorological stations in the Jianghuai region, the extreme-precipitation indices were calculated. The variations in 12 extreme-precipitation indices were detected by using the Mann–Kendall test in the Jianghuai region. The periodicity of indices was examined by wavelet analysis detecting significant time sections. Through the cross wavelet transform and wavelet coherence analyses, the nonlinear connections between extreme precipitation and atmospheric circulation were explored. The results indicate significant increasing trends in the max one-day precipitation amount (Rx1day), extreme wet days (R99p), and simple precipitation intensity index (SDII). The intensity of extreme precipitation increased significantly. The variation in extreme precipitation showed different trends in different regions, with a greater likelihood of increasing extreme-precipitation intensity and frequency in the southern region compared to the central and northern regions. The period of most oscillations of the indices tend toward be on a time scale of 2–4 years and are in the 1990s. The number of heavy precipitation days (R10 mm) and number of very heavy precipitation days (R20 mm) had, mainly, periods of 5.84 years. Additionally, there were significant resonance periods between the extreme-precipitation indices and the atmospheric circulation index; however, there were obvious differences in time domains. The North Atlantic Oscillation (NAO) and East Asian summer monsoon (EASM) had the most significant effect on the duration of extreme precipitation; Atlantic Oscillation (AO) and EASM had the most significant influence on the extreme-precipitation intensity. The results of the study can provide a scientific basis for water-resource management and disaster prevention and control in the Jianghuai region.

Evaluation and multimodel projection of seasonal precipitation extremes over central Asia based on CMIP6 simulations

AbstractCentral Asia faces an increasing challenge related to water resources arising from severe droughts and floods that have impacted the region in the past decades. Using a comprehensive set of extreme precipitation indices, we assess the performance of CMIP6 models in representing observed precipitation extremes and investigate future responses of these extremes to greenhouse gas emissions under four shared socioeconomic pathways (SSP). Particularly, this study identifies robust signals of projected changes in spring and summer precipitation extremes over central Asia. Results show that the CMIP6 models reasonably reproduced the spatial distribution and variability of precipitation extremes over southern central Asia (SCA) and northern central Asia (NCA) during the spring and summer seasons, respectively. Across the SSPs, the CMIP6 models project a decrease in total spring wet‐day precipitation amount (PRCPTOT) over SCA and a significant increase in PRCPTOT over the NCA. The projected changes are characterized by a significant increase in maximum consecutive 5‐day precipitation (RX5day), wet‐day intensity (SDII), and the number of heavy precipitation days (R10mm); these suggest that the spring will be characterized by intense precipitation extremes. Moreover, no significant change is projected for consecutive wet days (CWD) and dry spells (CDD) over SCA. Nonetheless, the CMIP6 models project a significant increase in summer PRCPTOT over SCA and a decrease in summer PRCPTOT, RX5day, R10mm, SDII, and CWD over NCA while projecting a significant and robust increase in dry spells. It is also found that the severity of the projected dry spell deepens across the SSP spectra and gets more pronounced towards the end of the 21st century. Notably, the intermodel spread of the precipitation extremes is smaller during the spring over SCA, larger during the summer season over NCA, and more probable in the warmer future.

Detectable Human Influence on Changes in Precipitation Extremes Across China

AbstractThis study explored the human influence on the observed changes in precipitation extremes across China for the period of 1961–2014 by the latest simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Changes in observations and CMIP6 simulations under different external forcings for annual total wet‐day precipitation (PRCPTOT), the number of heavy precipitation days (R10 mm), very wet days (R95p), extremely wet days (R99p), the maximum 1‐day precipitation amount (RX1day), and the maximum 5‐day precipitation amount (RX5day) are compared using a regularized optimal fingerprinting method. The results show that both anthropogenic (ANT) and greenhouse gas (GHG) experiments generally exhibit increasing trends in precipitation extremes over China, agreeing with the overall observed increases. The ANT influence is robustly detected for R95p, R99p, RX1day, and RX5day over China, with clear separation from the natural forcing. Additionally, the GHG signal is also detectable for R95p, R99p, and RX1day but separated from the natural and other ANT forcings for only R99p and RX1day and from the natural and ANT aerosol forcings for only RX5day. Thus, the intensified precipitation extremes can be attributed to human influence dominated by the GHG effect. However, detections fail for PRCPTOT and R10 mm. The ANT aerosol forcing may partly offset the role of the GHG responses to observed increases despite being undetectable. Based on the detection and attribution results, the observation‐constrained projections indicate that precipitation extremes are projected to significantly increase under the SSP2–4.5 and SSP5–8.5 scenarios but with a smaller magnitude compared to the raw CMIP6 outputs.

Spatial and Temporal Variations of Extreme Precipitation in Central Asia during 1982–2020

As one of the largest arid and semi-arid regions in the world, central Asia (CA) is very sensitive to changes in regional climate. However, because of the poor continuity of daily observational precipitation records in CA, the spatial and temporal variations of extreme precipitation in recent decades remain unclear. Considering their good spatial and temporal continuity, gridded data, such as Climate Prediction Center (CPC) global precipitation, and reanalysis data, such as ERA-Interim (ERA), are helpful for exploring the spatial–temporal variations of extreme precipitation. This study evaluates how well CPC and ERA can represent observed precipitation extremes by comparing the differences in eight extreme precipitation indices and observation data at 84 meteorological stations. The results indicate that the CPC (except for 1979–1981) is more suitable for depicting changes in precipitation extremes. Based on the CPC data for the period 1982–2020, we found that seven indices of precipitation extremes, including consecutive wet days (CWD), max1-day precipitation amount (Rx1day), max5-day precipitation amount (Rx5day), number of heavy precipitation days (R10), very wet days (R95p), annual total precipitation in wet days (PRCPTOT), and simple precipitation intensity index (SDII) have increased by 0.2 d/10a, 0.9 mm/10a, 1.8 mm/10a, 0.3 d/10, 8.4 mm/10a, 14.3 mm/10a and 0.1 mm/d/10a, respectively, and the consecutive dry days (CDDs) have decreased by −3.10 d/10a. It is notable that CDDs decreased significantly in the north of Xinjiang (XJ) but increased in Kyrgyzstan (KG), Tajikistan (TI), and eastern Turkmenistan (TX). The other indices increased clearly in the west of XJ, north of Kazakhstan (KZ), and east of KG but decreased in the south of KG, TI, and parts of XJ. For most indices, the largest change occurred in spring, the main season of precipitation in CA. Therefore, the large-scale atmospheric circulation in April is analyzed to contrast between the most and least precipitation years for the region. A typical circulation pattern in April for those extremely wet years includes an abnormal low-pressure center at 850 hpa to the east of the Caspian Sea, which enhances the southerly winds from the Indian Ocean and hence the transportation of water vapor required for precipitation into CA. This abnormal circulation pattern occurred more frequently after 2001 than before, thus partly explaining the recent increasing trends of precipitation extremes in CA.

Changes in extreme precipitation across South Asia for each 0.5 °C of warming from 1.5 °C to 3.0°C above pre-industrial levels

Motivated by the Paris Agreement, this study aims to investigate the changes in precipitation extremes across South Asia and its five climatic zones for each 0.5 °C of warming above the pre-industrial level from 1.5 °C to 3.0 °C. In this regard, 20 global climate model outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) are used. Four widely used extreme precipitation indices such as number of consecutive wet days (CWD), number of heavy precipitation days (R10mm), maximum consecutive 5-day precipitation (RX5day), and the number of very wet days (R99pTOT) defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) are applied to detect the extreme events. To ensure reduced uncertainty, we used bias-corrected and multi-model ensemble outputs. Results indicate that as the degree of global warming increases, the changes in magnitudes of extreme events are projected to intensify (except for CWD) and the largest growth is found under 3.0 °C in the entire domain and its five climatic zones. The polar climatic zone is anticipated to experience the highest magnitude of changes for extreme events under all the warming scenarios. The largest percentage of area with significant increase is found under the highest warming level (3.0 °C), especially for R95pTOT (95.5% of the entire domain). For an additional 0.5 °C (2.0–1.5 °C) of warming, the anticipated precipitation-related extremes will increase by 3.5%, (RX5day), 3.6% (R10mm) and 6.6% (R99pTOT), as mitigation targets set out in the Paris Agreement. The projected intense sea surface temperature over the Arabian Sea, and large water vapor content over South Asian landmass are expected to influence precipitation-related extreme events. Notably, considering all aspects of our analysis, R99pTOT is expected to be the most predominant extreme precipitation index across the domain with continued warming. So, our findings strongly support the Paris Agreement target to limit global warming to 1.5 °C and provide a scientific basis.

Variations in the precipitation extremes over the Guangdong-Hong Kong-Macao Greater Bay Area in China

By using the gridded 0.25° × 0.25° observation dataset of CN05.1 provided by the China Meteorological Administration, this study investigates the variations in nine extreme precipitation indices over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China from 1961 to 2018. Based on trends and interannual variations, the nine kinds of extreme precipitation are classified into four categories: category 1 includes very wet days (R95P), extremely wet days (R99P), the maximum 1-day precipitation amount (RX1day), and the maximum 5-day precipitation amount (RX5day). Category 2 is the number of heavy precipitation days (R10day), the number of very heavy precipitation days (R20day), and the simple daily intensity index (SDII). Categories 3 and 4 are consecutive wet days (CWDday) and consecutive dry days (CDDday), respectively. For the extreme precipitation in category 1, the abrupt change point from fewer to more values occurs in 1991 in summer. Three abrupt change points, from fewer to more in 1972 and 2009 and from more to fewer in 1994, occur in spring. For the extreme precipitation in category 2, the abrupt change point from fewer to more values occurs in 1993 in summer. Three abrupt change points, from fewer to more in 1965 and 2010 and from more to fewer in 1990, occur in spring. Annually and seasonally, abrupt changes occur in the early 2010s for CWDday which clearly increase and for CDDday which clearly decrease. In addition, CWDday exhibit abrupt change points from fewer to more in 1966 and from more to fewer in 1983 in spring. The variations in these extreme precipitation events have significant periodic oscillations of 3–5 years, quasi-8 years, or 8–14 years. During periods 1961–1994, 1995–2009, and 2010–2018, the changes in the annual and most seasonal R95P, R99P, R10day, R20day, and SDII are consistent with those of precipitation. The values in the latter period are increasing compared with those in the former stage. The changes in RX1day, RX5day, CWDday, and CDDday have their own characteristics.

Elevation-Dependent Trends in Precipitation Observed over and around the Tibetan Plateau from 1971 to 2017

The Tibetan Plateau (TP) are regions that are most sensitive to climate change, especially extreme precipitation changes with elevation, may increase the risk of natural disasters and have attracted attention for the study of extreme events in order to identify adaptive actions. Based on daily observed data from 113 meteorological stations in the Tibetan Plateau and the surrounding regions in China during 1971–2017, we calculated the annual total precipitation and extreme precipitation indices using the R ClimDex software package and explored elevation-dependent precipitation trends. The results demonstrate that the annual total precipitation increased at a rate of 6.7 mm/decade, and the contribution of extreme precipitation to total precipitation increased over time, and the climate extremes were enhanced. The annual total, seasonal precipitation, and precipitation extreme trends were observed in terms of elevation dependence in the Tibetan Plateau (TP) and the surrounding area of the Tibetan Plateau (TPS) during 1971–2017. There is growing evidence that the elevation-dependent wetting (EDWE) is complex over the TP. The trends in total precipitation have a strong dependence on elevation, and the EDWE is highlighted by the extreme precipitation indices, for example, the number of heavy precipitation days (R10) and consecutive wet days (CWD). The dependence of extreme precipitation on elevation is heterogeneous, as other extreme indices do not indicate EDWE. These findings highlight the precipitation complexity in the TP. The findings of this study will be helpful for improving our understanding of variabilities in precipitation and extreme precipitation in response to climate change and will provide support for water resource management and disaster prevention in plateaus and mountain ranges.

Assessment of GPM-Era Satellite Products’ (IMERG and GSMaP) Ability to Detect Precipitation Extremes over Mountainous Country Nepal

The reliability of satellite precipitation products is important in climatic and hydro-meteorological studies, which is especially true in mountainous regions because of the lack of observations in these areas. Two recent satellite rainfall estimates (SREs) from Global Precipitation Measurement (GPM)-era—Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-V06) and gauge calibrated Global Satellite Mapping of Precipitation (GSMaP-V07) are evaluated for their spatiotemporal accuracy and ability to capture extreme precipitation events using 279 gauge stations from southern slope of central Himalaya, Nepal, between 2014 and 2019. The overall result suggests that both SREs can capture the spatiotemporal precipitation variability, although they both underestimated the observed precipitation amount. Between the two, the IMERG product shows a more consistent performance with a higher correlation coefficient (0.52) and smaller bias (−2.49 mm/day) than the GSMaP product. It is worth mentioning that the monthly gauge-calibrated IMERG product yields better detection capability (higher probability of detection (POD) values) of daily precipitation events than the daily gauge calibrated GSMaP product; however, they both show similar performance in terms of false alarm ratio (FAR) and critical success index (CSI). Assessment based on extreme precipitation indices revealed that the IMERG product outperforms GSMaP in capturing daily precipitation extremes (RX1Day and RX5Day). In contrast, the GSMaP product tends to be more consistent in capturing the duration and threshold-based precipitation extremes (consecutive dry days (CDD), consecutive wet days (CWD), number of heavy precipitation days (R10mm), and number of extreme precipitation days (R25mm)). Therefore, it is suggested that the IMERG product can be a good alternative for monitoring daily extremes; meanwhile, GSMaP could be a better option for duration-based extremes in the mountainous region.

Trend analysis of extreme precipitation indices in the southern part of Java

Global warming causes changing on climate system which driving extreme climate and metrological event. The occurrence of extreme event, such as extreme precipitation can lead to flooding and drought which can have an impact on agriculture production. To observe the extreme event, Expert Team for Climate Change Detection Indices (ETCCDI) developed 27 extreme indices which are 11 extreme precipitation indices and 16 extreme temperature indices. In this study, we analyze the trend of extreme precipitation indices to understand the changing of precipitation in Southern part of Java. Extreme precipitation indices were analyzed using Climpact2. Trend significance of extreme indices time series were calculated using non-parametric Mann-Kendall Test. Spatial distribution of the annual trend in each index illustrated using IDW interpolation. Most of significant trend in extreme precipitation indices occurred in North West station of study area. Negative significant trend only happened on consecutive wet days (CWD) index that means wet days was decrease. Positive significant trend happened in every index exclude consecutive dry days (CDD), Number of heavy precipitation days number of heavy precipitation days (R10mm), and PRCPTOT. Precipitation in study area occur with high intensity in short period.

The Modulation of Daily Southern Africa Precipitation by El Niño–Southern Oscillation across the Summertime Wet Season

AbstractThe spatiotemporal evolution of daily southern Africa precipitation characteristics, and associated atmospheric circulation, related to El Niño and La Niña is examined across the region’s November–April wet season. Precipitation characteristics are examined in terms of monthly changes in daily average precipitation, the number of precipitation days, and the number of heavy precipitation days in three independently constructed estimates of observed precipitation during 1983–2018. Mechanisms related to precipitation changes, including contributions from mass divergence, water vapor transports, and transient eddies, are diagnosed using the atmospheric moisture budget based on the ERA5 reanalysis. El Niño is related to precipitation anomalies that build during December–March, the core of the rainy season, culminating in significantly below average values stretching across a semiarid region from central Mozambique to southeastern Angola. A broad anticyclone centered over Botswana drives these precipitation anomalies primarily through anomalous mass divergence, with moisture advection and transient eddies playing secondary roles. La Niña is related to significantly above average daily precipitation characteristics over all Africa south of 20°S in February and much less so during the other five months. February precipitation anomalies are primarily driven through mass divergence due to a strong anomalous cyclonic circulation, whereas a similar circulation is more diffuse during the other months. The spatiotemporal evolutions of anomalies in daily precipitation characteristics across southern Africa related to El Niño and La Niña are not equal and opposite. The robustness of an asymmetric evolution, which could have implications for subseasonal forecasts, needs to be confirmed with analysis of additional empirical data and established with climate model experimentation.