Future Changes In Precipitation Extremes Research Articles

Intensified response of extreme precipitation to rising temperature over the Tibetan Plateau from CMIP6 multi-model ensembles

This study conducted bias correction of the high-performing general circulation model (GCM) data in combination with observed data based on the latest GCM data released by the Coupled Model Intercomparison Project Phase 6 (CMIP6). The ability of GCMs to simulate the characteristics of extreme precipitation over the Tibetan Plateau (TP) was systematically assessed, and the CMIP6 multi-model ensemble was used to accurately project extreme precipitation. Furthermore, the response of extreme precipitation over the TP to global warming was explored. The results demonstrated that daily translation efficiently addressed the issue of apparent overestimation in the model’s raw output, and CMIP6 multi-model ensemble mean exhibited better simulation performance for all extreme precipitation indices over the TP. Additionally, the simulation performance of each index exhibited a divergence across various elevation bands as altitude increased, with the best and worst performance observed at the altitude bands of 1500–2000 m and above 5000 m, respectively. For future changes in extreme precipitation, the indices representing the amount and intensity of extreme precipitation increase significantly, whereas the indices representing the duration of precipitation decrease. Moreover, the plateau will show a spatial pattern of “wet regions becoming dry and dry regions becoming wet”. The relationship between extreme precipitation and temperature follows a positive and peak pattern over most areas of the plateau, while the response of extreme precipitation to temperature will increase by 0.6 %–52.6 % with rising temperature. The present results have important implications for investigating the impacts of climate change on the water cycle in alpine regions.

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

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

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

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

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

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

Future projections of extreme precipitation in Tropical America and Panama under global warming based on 150‐year continuous simulations using 20‐km and 60‐km atmospheric general circulation models

AbstractThis study explores future changes in extreme precipitation across tropical America and Panama using 150‐year continuous experiments run on the Meteorological Research Institute atmospheric general circulation model at horizontal resolutions of 20 km (AGCM20) and 60 km (AGCM60). Tropical America is a warming hotspot where the mean seasonal precipitation amounts are decreasing, but annual maximum daily precipitation amounts are following an increasing trend. In Panama, future climates simulated using AGCM20 and AGCM60 show an increase in December–January–February (DJF) precipitation over eastern Panama, and a decrease in June–July–August (JJA) precipitation over western areas near the Pacific coast. Annual maximum daily precipitation increases on the eastern side of the country in both models. The mean annual maximum daily precipitation for tropical America shows decadal oscillations, similar to the global mean but with smaller amplitude variations. The mean annual maximum daily precipitation for Panama shows large oscillations, even for the 10‐year running mean. For tropical America, the mean annual maximum daily precipitation has a linear relationship with surface air temperature that is independent of the Representative Concentration Pathway scenarios, but this is not the case for Panama. Temporal correlations between the tropical America mean annual maximum daily precipitation and annual mean surface air temperature in the historical experiments have a geographical distribution similar to that of the sea surface temperature anomalies during negative El Niño/Southern Oscillation events, but not in the future experiments. The correlation between the Panama mean annual maximum daily precipitation and surface air temperature in both the historical and future experiments has a geographical distribution similar to that of the temporal correlation for the tropical America mean in the historical experiment. The annual maximum daily precipitation in Panama is projected to increase during negative El Niño events in the future, as observed in the historical climate.

Future extreme precipitation changes in the South Asian Summer Monsoon Dominance Region

This study aims to investigate the future changes in precipitation extremes across the South Asian summer monsoon domain using the latest CMIP6 global climate model (GCM) outputs under four SSP-RCPs for the near future (2031–2065) and the far future (2066–2100). The models were bias-corrected using nonparametric quantile transformation based on the smoothing spline technique. Future analysis was done in a more robust and systematic way by using seasonal-based as well as regional-based approaches. Two seasons, pre-monsoon from March to May (MAM) and monsoon from June to September (JJAS) were considered for seasonal assessment. The study area was divided into subzones based on Köppen-Geiger's climate structure for regional assessment. Five extreme precipitation indices, which describe extreme precipitation events, R95pTOT, Rx1day, Rx5day, PRCPTOT and SDII from Expert Team on Climate Change Detection and Indices (ETCCDI), were applied. The evaluation results reveal that the performance of the bias correction method differs based on the extreme precipitation index, climate zone, and season. Based on the model agreement, a negative shift in the precipitation extremes in the future will be dominant during the pre-monsoon season, particularly in the subzones of Tropical rainforest (southern regions of Sri Lanka), Tropical monsoon (Western Ghats of India, coastal regions of Myanmar, regions of Bangladesh), Tropical savannah (regions of Myanmar), and Arid steppe hot (southern regions of India, central parts of Myanmar). During monsoon season, both negative and positive shifts will prevail in the subzones, and the precipitation extremes during the far future will be more intensified than near future in most of the subzones. Additionally, the shifts will tend to decline from SSP1–2.6 to SSP5–8.5 in the near future and increment from SSP1–2.6 to SSP5–8.5 in the far future.

Kilometer-Scale Multimodel and Multiphysics Ensemble Simulations of a Mesoscale Convective System in the Lee of the Tibetan Plateau: Implications for Climate Simulations

Abstract Kilometer-scale climate model simulations are useful tools to investigate past and future changes in extreme precipitation, particularly in mountain regions, where convection is influenced by complex topography and land–atmosphere interactions. In this study, we evaluate simulations of a flood-producing mesoscale convective system (MCS) downstream of the Tibetan Plateau (TP) in the Sichuan basin from a kilometer-scale multimodel and multiphysics ensemble. The aim is to better understand the physical processes that need to be correctly simulated for successfully capturing downstream MCS formation. We assess how the ensemble members simulate these processes and how sensitive the simulations are to different model configurations. The preceding vortex evolution over the TP, its interaction with the jet stream, and water vapor advection into the basin are identified as key processes for the MCS formation. Most modeling systems struggle to capture the interaction between the vortex and jet stream, and perturbing the model physics has little impact, while constraining the large-scale flow by spectral nudging improves the simulation. This suggests that an accurate representation of the large-scale forcing is crucial to correctly simulate the MCS and associated precipitation. To verify whether the identified shortcomings systematically affect the MCS climatology in longer-term simulations, we evaluate a 1-yr WRF simulation and find that the seasonal cycle and spatial distribution of MCSs are reasonably well captured and not improved by spectral nudging. While the simulations of the MCS case highlight challenges in extreme precipitation forecasting, we conclude that these challenges do not systematically affect simulated climatological MCS characteristics. Significance Statement Convective storm systems in mountain regions are not well understood, because the spatial resolution in conventional regional climate models is too coarse to resolve relevant processes. Here, we evaluate high-resolution climate model simulations of a storm system on the downwind side of the Tibetan Plateau. Understanding which models and model setups work well to represent this type of storm system is important because high-resolution models can help us understand mechanisms of storm formation in mountain regions and how climate change will affect these. A key finding is that most of the models struggle to capture the selected storm case, while a 1-yr simulation shows that the general statistics of storm systems around the Tibetan Plateau are still reasonably well captured.

The intensification of short-duration rainfall extremes due to climate change – Need for a frequent update of intensity–duration–frequency curves

Numerous studies have investigated future changes in extreme precipitation by employing climate models. However, trends in extreme short-duration (20–180 min) rainfall intensities, that is, design storm rainfall intensities used for stormwater infrastructure design, have received considerably less attention despite their importance. This study quantified the effect of climate change on design storm intensity based on observations. Trends were investigated for diverse short-term rainfall durations, ranging from 20 to 180 min, at 11 meteorological stations in Northern Europe, Estonia. The Mann-Kendall test and Sen’s slope estimator were used to detect trends and calculate the trend magnitudes by analyzing a 70 year period of high-resolution temporal observed rainfall data. The Sen’s slope indicated a positive magnitude of slopes for all the durations and stations investigated, and two stations in Northeast Estonia showed significantly increasing trends (p < 0.05). Unsurprisingly, the spatial distribution of trends revealed notable variations from station to station, as extreme rainfall events are heavily localized due to cloudburst. Despite this variety, it can be generalized that the annual maximum rainfall intensities in Estonia have increased at an average rate of 4 % per decade due to changing climate conditions, invariant of the rainfall duration analyzed. The use of decades enables to easily keep the intensity–duration–frequency curves updated, and it is more practical and relatable for engineers as well. The findings of this study provide critical knowledge for urban water management decision-makers, indicating the need to frequently update urban design criteria to design climate resilient urban infrastructure.

Introducing NARCliM1.5: Evaluation and projection of climate extremes for southeast Australia

NARCliM1.5 is the second generation of New South Wales and Australian Regional Climate Modelling (NARCliM) project, which is designed to produce an ensemble of regional climate projections for CORDEX Australasia and southeast Australia. The selected global climate models (GCMs) are used to drive Weather Research and Forecasting (WRF) model for dynamical downscaling. In this study, we evaluate how well the two generations of NARCliM (N1.0 and N1.5) represent the observed record for 12 selected climate extremes and examine their projected changes for southeast Australia. N1.5 ensemble substantially improves upon N1.0 in capturing the spatial patterns of precipitation extremes, however, there is little difference between the two ensembles for temperature extremes. Both N1.0 and N1.5 underestimate dry extreme (consecutive dry day - CDD), hot extremes (warmest daily maximum temperature – TXx, and number of days when maximum temperature is greater than 35 °C - TXge35) and daily temperature range (DTR) but overestimate wet extremes (annual sum of daily precipitation above 99th percentile - R99p, consecutive wet days - CWD, days when precipitation is at least 10 mm - R10mm, maximum 1-day precipitation - Rx1Day and total wet day precipitation -PRCPTOT) and cold extreme (coldest daily minimum temperature - TNn). N1.0 and N1.5 project different spatial patterns of future changes in precipitation extremes but similar changes in temperature extremes. Differences in climate extremes between N1.0 and N1.5 could be attributed to the driving GCMs. The combined future projections of both N1.0 and N1.5 provide a more complete sampling of the future change space. Future projections indicate that precipitation and temperature extremes will become more intense, raising implications for future planning and risk management.

Future Characteristics of Extreme Precipitation Indicate the Dominance of Frequency Over Intensity: A Multi‐Model Assessment From CMIP6 Across India

AbstractThis study presents a comprehensive analysis on the past and future changes in precipitation extremes and quantifies the relative contributions from its frequency and intensity across India, considering five extremeness levels, denoted by 95th, 99th, 99.9th, 99.95th, and 99.97th percentile. Gridded station‐based observations from the historical period (1951–2020) and simulations from 14 General Circulation Models for the future (2021–2100), participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) are considered. Apart from an overall increasing pattern of precipitation extremes, it is noticed that the contribution of frequency dominates over intensity. Specifically, the frequency of 99th percentile daily rainfall is projected to increase approximately by two‐ (SSP245) to three‐ (SSP585) times in future. We also proposed a new zoning of entire Indian mainland, identified as six Homogeneous Precipitation Zones (HPZs). HPZ‐wise analysis reveals that the increase in frequency dominates over intensity for all the HPZs with a varying extent. For instance, increase in frequency is more for the climatologically high‐precipitation zones (HPZ‐3: Western Ghats, and HPZ‐6: North‐east India), whereas increase in intensity is more for the low‐precipitation zones (HPZ‐1: North‐west India, and HPZ‐4: Peninsular India). The degree of increase gets even more pronounced under the worst scenario SSP585, indicating a potential impact of anthropogenic activities on changing precipitation extremes. Findings of this study should be accounted in the climate change adaptation and mitigation strategies for future.

Future changes in extreme precipitation from 1.0 °C more warming in the Tienshan Mountains, Central Asia

The Tienshan Mountains is the main water source and ecological barrier in the central portion of the Silk Road Economic Belt, a new economic development zone with the Asia-Pacific Economic Circle to the east and the European Economic Circle to the west. Production-living-ecological activities in the arid Central Asia region are heavily dependent on water resources mainly recharged from melt and alpine precipitation. Hence, reliable projections of changes in extreme precipitation under global warming are particularly important for the utilization and management of water resources. Based on the downscaled and bias-corrected state-of-art global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigate changes in extreme precipitation over the Tienshan Mountains, Central Asia (TMCA) under different levels of global warming (1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C). We specifically assess the robustness of changes and the benefits of limiting warming to 2.0 °C as opposed to 3.0 °C. Compared with the reference period (1976–2005), a robust change in extreme precipitation across the TMCA is expected for all warming levels. And the fraction of land faced a robust change also increases with warming levels. Furthermore, there would be a substantial rise in extreme impacts in the TMCA when shifting from increases of 2.0 °C to 3.0 °C. In a scenario involving a 1.0 °C rise (i.e., from 2.0 °C to 3.0 °C), nearly 85.70 % and 60.19 % of the land in the TMCA will be affected by a robust increase in annual total wet-day precipitation (PRCPTOT) and number of light rain days (R5mm), respectively. In the same scenario, areas affected by robust changes in duration indices (consecutive dry days [CDD] and consecutive wet days [CWD]) will likely be less than 11.59 %. Limiting warming to 2.0 °C instead of 3.0 °C can avoid a marked increased impacts of about 62.84 %∼153.77 % of the change in frequency, intensity, and duration of extreme precipitation.

Future changes in extreme precipitation in central Asia with 1.5–4°C global warming based on Coupled Model Intercomparison Project Phase 6 simulations

AbstractQuantifying future precipitation changes in central Asia (CA), one of the largest semiarid‐to‐arid regions in the world, is increasingly attracting attention. Extreme precipitation changes in CA have been studied based on global climate models (GCMs) in the Coupled Model Intercomparison Project (CMIP). However, quantitative information regarding projected changes in extreme precipitation in CA under the 1.5–4°C global warming scenario is lacking; the potential advantages of the latest‐generation GCMs (i.e., CMIP6) in characterizing precipitation in CA have not been investigated. Thus, in this study, we evaluated the models' overall performance in reproducing the spatiotemporal pattern of four extreme precipitation indices and further performed an observationally constrained projection of changes in extreme precipitation indices for 1.5–4°C global warming based on a weighted multimodel ensemble. Regarding homologous models, CMIP6 models exhibited limited improvement relative to their earlier versions in CMIP5. We project that extreme precipitation indices in CA will increase approximately linearly as global warming increases, except for the consecutive dry days (CDD) index. The changes in precipitation intensity and accumulation exhibit robust consistency between models, whereas the signal of CDD changes is masked by the noise produced by intermodel uncertainties. The changes in the average annual accumulated and maximum 1‐day precipitation relative to the reference period (1981–2010) in CA are 12.0 and 14.2% at 3°C global warming (similar to late‐century warming projected based on current mitigation policies), respectively. Moreover, we demonstrate the advantage of the weighted scheme over the traditional unweighted scheme for multimodel ensemble projection.

Increased Large‐Scale Convective Aggregation in CMIP5 Projections: Implications for Tropical Precipitation Extremes

AbstractConvective aggregation refers to the clustering of convective events and occurs on a wide range of spatial scales. It has been suggested that the behavior of convective aggregation may change under global warming, with potential implications for future changes in precipitation extremes. Here, convective regions of the tropics are defined from a percentile threshold on gridded daily precipitation data and used to quantify large‐scale convective aggregation in an ensemble of global climate models. Applying three separate indices for aggregation, it is found that large‐scale convective aggregation increases in 17 of the 19 analyzed models under future warming. However, aggregation is found not to be correlated with tropical‐mean precipitation extremes, either climatologically or with respect to the sensitivity to warming. The large model spread in aggregation indices across the ensemble suggests the possible utility of large‐scale convective aggregation as a target for model evaluation.

Future changes in extreme precipitation over the San Francisco Bay Area: Dependence on atmospheric river and extratropical cyclone events

Extreme precipitation poses a major challenge for local governments, including the City and County of San Francisco, California, as flooding can damage and destroy infrastructure and property. As the climate continues to warm, reliable future precipitation projections are needed to provide the best possible information to decision makers. However, future changes in the magnitude of extreme precipitation are uncertain, as current state-of-the-art global climate models are typically run at relatively coarse horizontal resolutions that require the use of convective parameterization and have difficulty simulating observed extreme rainfall rates. Here, we performed ensembles of convection-permitting regional climate model simulations to investigate how five historically impactful extreme precipitation events over the San Francisco Bay Area could change if similar events occurred in future climates. We found that changes in storm-total precipitation depend strongly on storm type. Precipitation associated with an atmospheric river (AR) accompanied by an extratropical cyclone (ETC) is projected to increase at a rate exceeding (by up to 1.5 times) the theoretical Clausius Clapeyron scaling of 6–7% per °C warming. On the other hand, future precipitation changes are weak or negative for events characterized by an AR only, despite increases in precipitable water and integrated vapor transport that are similar to those of the co-occurring AR and ETC events. The differences in the sign of future precipitation change between AR-only events and co-occurring AR and ETC events is instead linked with changes in mid-tropospheric vertical velocity. Given that the majority of observed ARs are associated with an ETC, this research has important implications for future precipitation impacts over the Bay Area, as it indicates that storm-total precipitation associated with the most common type of storm event may increase by up to 26–37% in 2100 relative to historical.

Anticipated Changes in Alaska Extreme Precipitation

Abstract Flooding from extreme precipitation can have major impacts on society in Alaska. Understanding how these extremes may change in the future is needed for better planning under climate change. Data on future changes in extreme precipitation over Alaska from dynamically downscaled output of two global climate models (GFDL and CCSM) were employed in this study. Threshold amounts for duration of the precipitation event (1 h, 1 day, and 30 days) and return intervals (2, 10, and 50 years) are evaluated and further downscaled onto NOAA Atlas 14. For each duration and return interval, the models’ fractional changes of threshold amounts are applied to the Atlas 14 estimates to remove the model bias. The threshold amounts for nearly all event durations and return intervals are projected to increase from present (1979–2005) amounts to higher values in later decadal periods (2020–49, 2050–79, and 2080–99), and the percentage increases generally exceed the changes in the mean amounts. The percentage increases are comparable in the various geographical regions of Alaska, but the increases in the actual amounts are greatest in the wetter southeast. Although the downscaled GFDL model shows larger increases than the CCSM model in amounts for nearly all durations and return intervals, both models indicate that convective precipitation will become an increasingly greater fraction of the total precipitation during the warm season. The increase in the proportion of convective precipitation is consistent with the more rapid increase in extreme amounts than in mean amounts.

Future Changes in Extreme Precipitation and Their Association with Tropical Cyclone Activity over the Western North Pacific and East Asia in 20 km AGCM Simulations

Future Changes in Extreme Precipitation and Their Association with Tropical Cyclone Activity over the Western North Pacific and East Asia in 20 km AGCM Simulations

Significant Increase of the Global Population Exposure to Increased Precipitation Extremes in the Future

AbstractPrecipitation extremes and associated hazards often cause agricultural losses and infrastructure damage and even exert negative impacts on human health. It is thus crucial to assess future changes in precipitation extremes and exposure under future warming scenarios to improve the mitigation of climate change. On the basis of Coupled Model Intercomparison Project phase 6 simulations, we find that occurring probabilities of precipitation extremes that exceed the 99th percentile threshold are projected to increase over global lands in the coming century, especially under the no‐mitigation scenario of SSP5‐8.5. Under this scenario, by the end of this century, occurrences of aggregations of precipitation extremes over global lands are expected to increase by approximately 1.8 times. Increases are also expected even if early mitigation is conducted via SSP1‐2.6. Accordingly, global aggregate exposure would increase by at least 50% in the future under SSP5‐8.5. Changes in precipitation extremes will exert a substantial influence on population exposure, of which contribution will be up to 46% under SSP5‐8.5 by the end of this century. The future maximum centers of exposure are concentrated over East Asia, South Asia, the Mediterranean basin, and eastern North America, as in the current state. However, the rapid increase in population over some parts of Africa plays a larger role than climate change and significantly increases the exposure in these regions. As a result, regions of Africa will become other high‐exposure global centers in the future. Climate change mitigation should be thus the key policy response to reduce population exposure in the future over most global regions.

Future Changes in Precipitation Extremes over East Africa Based on CMIP6 Models

This paper presents an analysis of projected precipitation extremes over the East African region. The study employs six indices defined by the Expert Team on Climate Change Detection Indices to evaluate extreme precipitation. Observed datasets and Coupled Model Intercomparison Project Phase six (CMIP6) simulations are employed to assess the changes during the two main rainfall seasons: March to May (MAM) and October to December (OND). The results show an increase in consecutive dry days (CDD) and decrease in consecutive wet days (CWD) towards the end of the 21st century (2081–2100) relative to the baseline period (1995–2014) in both seasons. Moreover, simple daily intensity (SDII), very wet days (R95 p), very heavy precipitation &gt;20 mm (R20 mm), and total wet-day precipitation (PRCPTOT) demonstrate significant changes during OND compared to the MAM season. The spatial variation for extreme incidences shows likely intensification over Uganda and most parts of Kenya, while a reduction is observed over the Tanzania region. The increase in projected extremes may pose a serious threat to the sustainability of societal infrastructure and ecosystem wellbeing. The results from these analyses present an opportunity to understand the emergence of extreme events and the capability of model outputs from CMIP6 in estimating the projected changes. More studies are recommended to examine the underlying physical features modulating the occurrence of extreme incidences projected for relevant policies.

Future changes in precipitation extremes across China based on CMIP6 models

AbstractA comparison assessment of model capabilities in simulating precipitation extremes across China was first implemented by using 30 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and using 36 CMIP6 models. The results indicate that the multi‐model median ensembles (MME) of both the CMIP5 and CMIP6 models can reasonably reproduce the climate means for the period from 1986 to 2005, and the biases are lower in most CMIP6 models compared to the CMIP5 models, especially over southern China. To provide further comparisons, 14 CMIP6 models are selected and compared with their predecessors in CMIP5. The results show that the CMIP6 models generally exhibit superior skill in simulating the extreme precipitation indices over China. The model spreads for most of the extreme indices in the CMIP6 version are also smaller. Additionally, the MMEs of the two CMIPs outperform individual models. However, some CMIP6 models also exhibit weaker skill levels in simulating some particular indices compared with those in CMIP5, which merits further investigation. The results from seven reanalyses further show large uncertainties for these indices; therefore, care should be taken in comparison with reanalyses. For future changes in precipitation extremes, total wet day precipitation (PRCPTOT), maximum 5‐day precipitation (RX5day) and very heavy precipitation days (R20mm) are projected to clearly increase across China over the coming century under the shared socioeconomic pathway (SSP) 2‐4.5 and SSP5‐8.5 scenarios. However, the dry condition index of CDD exhibits a decreasing tendency in the future, which implies that the dry conditions induced by precipitation anomalies will be mitigated. However, large uncertainties are still observed for future changes, which are primarily sourced from inter‐model and scenario variabilities, especially for the projected changes at the end of the 21st century.

Present and future diurnal hourly precipitation in 0.11° EURO-CORDEX models and at convection-permitting resolution

The diurnal cycle of precipitation (DCP) is a core mode of precipitation variability in regions and seasons where the dominant precipitation type is convective. The occurrence of extreme precipitation is often closely linked to the DCP. Future changes in extreme precipitation may furthermore, in certain regions, exhibit a strong diurnal signal. Here we investigate the present and future diurnal cycle of hourly precipitation in the state-of-the-art 0.11°C EURO-CORDEX (EC-11) ensemble and in a convection-permitting model (CPM), with a focus on extremes. For the present climate, long-standing timing and frequency biases in the DCP found in lower-resolution models persist in the EC-11 ensemble. In the CPM, however, these biases are largely absent, particularly the diurnal distribution of extremes, which the EC-11 ensemble misrepresents. For future changes to hourly precipitation, we find clear diurnal signals in the CPM and in EC-11 models, with high regional and intra-ensemble variability. The diurnal signal typically peaks in the morning. Interestingly, the EC-11 ensemble mean shows reasonable agreement with the CPM on the diurnal signal’s timing, showing that this feature is representable by models with parametrized convection. Comparison with the CPM suggests that EC-11 models greatly underestimate the amplitude of this diurnal signal. Our study highlights the advantages of CPMs for investigating future precipitation change at the diurnal scale, while also showing the EC-11 ensemble capable of detecting a diurnal signal in future precipitation change.