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The moving-window correlation analysis was applied to investigate the relationship between autumn Indian Ocean Dipole (IOD) events and the synchronous autumn precipitation in the Huaxi region (western China), China, based on daily precipitation, sea surface temperature (SST), and atmospheric circulation data from 1960 to 2012. The correlation curves of IOD and the early modulation of the Huaxi region ' s autumn precipitation indicated a behavioral change in the 1970s. Between 1960 and 1979, when the IOD was in a positive phase during autumn, circulations changed from a \"W\" shape to an \"M\" shape at 500 hPa in the middle-high latitude region in Asia. A northwest flow allowed a cold flux to reach Sichuan province. The positive anomaly of water vapor flux transported from the western Pacific to Huaxi region strengthened, causing a precipitation increase in the east Huaxi region. Between 1980 and 1999, when the IOD in autumn was also in the positive phase, the atmospheric circulation was a W\" shape at 500 hPa. In this scenario, the positive anomaly of the water vapor flux transported from the Bay of Bengal to Huaxi region strengthened, causing a precipitation increase in the west Huaxi region. In summary, the Indian Ocean switched from a cold phase to a warm phase during the 1970s, causing the instability of the inter-annual relationship between IOD and the autumn rainfall in Huaxi region.

According to observational evidence and climate model projections, the frequency and intensity of the rapid shift from drought to pluvial (rapid dry‒wet alternation, RDWA) increases as warming intensifies. Given that post‐drought precipitation is a key cause of RDWA, this study focuses on changes in post‐drought precipitation. Climate model projections indicate that the mean post‐drought precipitation will increase by 15.3% during 2071–2100 under the Shared Socio‐economic Pathway (SSP) 585 scenario. The scenario‐averaged response rate of post‐drought precipitation (4.6%/K) to global warming is significantly greater than that of general precipitation (2.7%/K). Furthermore, there will be an increase in the proportion of land area experiencing maximum post‐drought precipitation in autumn. The mean post‐drought extreme precipitation will increase by 20.5% under SSP585, exacerbating the severity of RDWA in a warmer world. The post‐drought thermodynamic (precipitable water) and dynamic (atmospheric vertical velocity) components are both conducive to the future increase in post‐drought precipitation. Plain Language Summary Post‐drought precipitation can directly cause the shift from drought to pluvial (dry‒wet alternation), leading to agricultural losses and natural hazards such as landslides. Therefore, understanding and quantifying how it will change in the future is important. Our results find that mean post‐drought precipitation will increase by about 15.3% and more than half of droughts will be accompanied by extreme precipitation in the following month by the end of the 21st century under high emission scenario. More maximum post‐drought precipitation will occur in autumn, potentially affecting agricultural activities and threatening food security in the future. The response of mean post‐drought precipitation to global warming is stronger than that of general precipitation. Our further analysis suggests that under a warming climate, post‐drought atmospheric conditions are favorable for rainfall formation after droughts. Key Points An increase in post‐drought precipitation will exacerbate the frequency and intensity of rapid dry‐wet alternations The future mean response of post‐drought precipitation to warming (4.5%–4.7%/K) is stronger than that of general precipitation (2.1%–3.1%/K) Increased precipitable water and atmospheric vertical velocity favor the increase in post‐drought precipitation

Extreme precipitation events are frequent under global warming, leading to severe social, economic, and environmental damages. Therefore, a coupled stepwise clustered copula downscaling (SCCD) method is developed to explore the spatial and temporal variability of extreme precipitation in Eastern China under two shared socioeconomic pathways (SSPs). The performance of SCCD in reproducing historical climatology is assessed by comparing its simulated values with the observed data. The result demonstrates that SCCD performs well in modeling climate variables. In addition, the seasonal variations, probability distributions, and absolute changes of nine extreme precipitation indices in the future periods (i.e., 2024—2060 and 2061—2100) are compared with their performance in the historical period (i.e., 1981—2020). The results show that the precipitation in Eastern China shows an increasing trend over time. For example, compared to the historical period, the mean annual precipitation increases by 9.4% and 13.6% for the periods 20424–2060 and 2060–2100 under SSP585, respectively. The spatial variability and age trends suggest that future precipitation extremes also show significant regional differences. By the end of the twenty-first century, both frequencies and intensities of extreme precipitation at high latitudes have changed significantly. Under both SSP scenarios, all nine indices (except Consecutive Dry Days) increase by more than 10% in the future period at the high latitudes, especially for Very heavy precipitation days (R20), where the increase is more than 50% in most areas. In addition, the impacts of different emission scenarios on precipitation in autumn and winter are significantly greater than those in spring and summer. The results can provide a scientific basis for decision-makers to develop mitigation and adaptation policies to minimize the risks caused by climate change.

In the coming few decades, projected increases in global temperature and humidity are generally expected to exacerbate human exposure to climate extremes (e.g., humid-heat and rainfall extremes). Despite the growing risk of humid-heat stress (measured by wet-bulb temperature), it has received less attention in East Africa, where arid and semi-arid climatic conditions prevail. Moreover, no consensus has yet been reached across models regarding future changes in rainfall over this region. Here, we screen Global Climate Models (GCMs) from CMIP5 and CMIP6 and use, for boundary conditions, simulations from only those GCMs that simulate successfully recent climatic trends. Based on these GCMs and Regional Climate Model (RCM) simulations, we project that annual mean temperature is likely to rise by 2 ℃ toward midcentury (2021–2050) ​at a faster rate than the global average (about 1.5 ℃), under the RCP8.5 and SSP5-8.5 scenarios, associated with more frequent and severe climate extremes. In particular, low-lying regions in East Africa will be vulnerable to severe heat stress, with an extreme wet-bulb temperature approaching or exceeding the US National Weather Service’s extreme danger threshold of 31 ℃. On the other hand, population centers in the highlands of Ethiopia will receive significantly more precipitation during the autumn season and will see more extreme rainfall events, with implications for flooding and agriculture. The robustness of these results across all GCM and RCM simulations, and for both of CMIP5 and CMIP6 frameworks (CMIP: Coupled Model Inter-comparison Project) supports the reliability of these future projections. Our simulations of near-term climate change impacts are designed to inform the development of sound adaptation strategies for the region.

Changes in precipitation over the European Alps are investigated with the regional climate model MAR (Modèle Atmosphérique Régional) applied with a 7 km resolution over the period 1903–2010 using the reanalysis ERA-20C as forcing. A comparison with several observational datasets demonstrates that the model is able to reproduce the climatology as well as both the interannual variability and the seasonal cycle of precipitation over the European Alps. The relatively high resolution allows us to estimate precipitation at high elevations. The vertical gradient of precipitation simulated by MAR over the European Alps reaches 33% km−1 (1.21 mm d−1 km−1) in summer and 38 % km−1 (1.15 mm d−1 km−1) in winter, on average, over 1971–2008 and shows a large spatial variability. A significant (p value < 0.05) increase in mean winter precipitation is simulated in the northwestern Alps over 1903–2010, with changes typically reaching 20 % to 40 % per century. This increase is mainly explained by a stronger simple daily intensity index (SDII) and is associated with less-frequent but longer wet spells. A general drying is found in summer over the same period, exceeding 20 % to 30 % per century in the western plains and 40 % to 50 % per century in the southern plains surrounding the Alps but remaining much smaller (<10 %) and not significant above 1500 m a.s.l. Below this level, the summer drying is explained by a reduction in the number of wet days, reaching 20 % per century over the northwestern part of the Alps and 30 % to 50 % per century in the southern part of the Alps. It is associated with shorter but more-frequent wet spells. The centennial trends are modulated over the last decades, with the drying occurring in the plains in winter also affecting high-altitude areas during this season and with a positive trend of autumn precipitation occurring only over the last decades all over the Alps. Maximum daily precipitation index (Rx1day) takes its highest values in autumn in both the western and the eastern parts of the southern Alps, locally reaching 50 to 70 mm d−1 on average over 1903–2010. Centennial maxima up to 250 to 300 mm d−1 are simulated in the southern Alps, in France and Italy, as well as in the Ticino valley in Switzerland. Over 1903–2010, seasonal Rx1day shows a general and significant increase at the annual timescale and also during the four seasons, reaching local values between 20 % and 40 % per century over large parts of the Alps and the Apennines. Trends of Rx1day are significant (p value < 0.05) only when considering long time series, typically 50 to 80 years depending on the area considered. Some of these trends are nonetheless significant when computed over 1970–2010, suggesting a recent acceleration of the increase in extreme precipitation, whereas earlier periods with strong precipitation also occurred, in particular during the 1950s and 1960s.

Mesoscale convective systems (MCSs) play an important role in modulating the global water cycle and energy balance and frequently generate high-impact weather events. The majority of existing literature studying MCS activity over East Asia is based on specific case studies and more climatological investigations revealing the precipitation characteristics of MCSs over eastern China are keenly needed. In this study, we use an iterative rain cell tracking method to identify and track MCS precipitation during 2008–16 to investigate regional differences and seasonal variations of MCS precipitation characteristics. Our results show that the middle-to-lower reaches of the Yangtze River basin (YRB-ML) receive the largest amount and exhibit the most pronounced seasonal cycle of MCS precipitation in eastern China. MCS precipitation over YRB-ML can exceed 2.6 mm day−1 in June, contributing over 30.0% of April–July total rainfall. Particularly long-lived MCSs occur over the eastern periphery of the Tibetan Plateau (ETP), with 25% of MCSs over the ETP persisting for more than 18 h in spring. In addition, spring MCSs feature larger rainfall areas, longer durations, and faster propagation speeds. Summer MCSs have a higher precipitation intensity and a more pronounced diurnal cycle except for southeastern China, where MCSs have similar precipitation intensity in spring and summer. There is less MCS precipitation in autumn, but an MCS precipitation center over the ETP still persists. MCSs reach peak hourly rainfall intensities during the time of maximum growth (a few hours after genesis), reach their maximum size around 5 h after genesis, and start decaying thereafter.

Climate change poses great risks to western Canada's ecosystem and socioeconomical development. To assess these hydroclimatic risks under high-end emission scenario RCP8.5, this study used the Weather Research Forecasting (WRF) model at a convection-permitting (CP) 4 km resolution to dynamically downscale the mean projection of a 19-member CMIP5 ensemble by the end of the 21st century. The CP simulations include a retrospective simulation (CTL, 2000–2015) for verification forced by ERA-Interim and a pseudo-global warming (PGW) for climate change projection forced with climate change forcing (2071–2100 to 1976–2005) from CMIP5 ensemble added on ERA-Interim. The retrospective WRF-CTL's surface air temperature simulation was evaluated against Canadian daily analysis ANUSPLIN, showing good agreements in the geographical distribution with cold biases east of the Canadian Rockies, especially in spring. WRF-CTL captures the main pattern of observed precipitation distribution from CaPA and ANUSPLIN but shows a wet bias near the British Columbia coast in winter and over the immediate region on the lee side of the Canadian Rockies. The WRF-PGW simulation shows significant warming relative to CTL, especially over the polar region in the northeast during the cold season, and in daily minimum temperature. Precipitation changes in PGW over CTL vary with the seasons: in spring and late autumn precipitation increases in most areas, whereas in summer in the Saskatchewan River basin and southern Canadian Prairies, the precipitation change is negligible or decreased slightly. With almost no increase in precipitation and much more evapotranspiration in the future, the water availability during the growing season will be challenging for the Canadian Prairies. The WRF-PGW projected warming is less than that by the CMIP5 ensemble in all seasons. The CMIP5 ensemble projects a 10 %–20 % decrease in summer precipitation over the Canadian Prairies and generally agrees with WRF-PGW except for regions with significant terrain. This difference may be due to the much higher resolution of WRF being able to more faithfully represent small-scale summer convection and orographic lifting due to steep terrain. WRF-PGW shows an increase in high-intensity precipitation events and shifts the distribution of precipitation events toward more extremely intensive events in all seasons. Due to this shift in precipitation intensity to the higher end in the PGW simulation, the seemingly moderate increase in the total amount of precipitation in summer east of the Canadian Rockies may underestimate the increase in flooding risk and water shortage for agriculture. The change in the probability distribution of precipitation intensity also calls for innovative bias-correction methods to be developed for the application of the dataset when bias correction is required. High-quality meteorological observation over the region is needed for both forcing high-resolution climate simulation and conducting verification. The high-resolution downscaled climate simulations provide abundant opportunities both for investigating local-scale atmospheric dynamics and for studying climate impacts on hydrology, agriculture, and ecosystems.

This paper reconsiders the precipitation concentration index (PCI) in Serbia using precipitation measurements such as the mean winter precipitation amount, annual total precipitation, mean summer precipitation amount, mean spring precipitation amount, mean autumn precipitation amount and the mean of precipitation for the vegetation period (April–September). Potentials for further improvement of PCI prediction lie in the improvement of current prediction strategies. One of the options is the introduction of model predictive control. To manage the PCI, it is good to select factors or parameters that are the most important for PCI estimation and prediction, i.e. to conduct variable selection procedure. In the present study, a regression based on the adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential PCI inputs based on the precipitation measurements. The effectiveness of the proposed strategy is verified according to the simulation results. The results show that the mean autumn precipitation amount is the most influential for PCI prediction and estimation and could be used for the simplification of predictive methods to avoid multiple input variables.

Monthly precipitation in Chile (30°–55°S) was found to vary by intensity, latitude, and longitude of the South Pacific high (SPH). In austral winter, precipitation was higher when the SPH was weaker and when it was centered farther west. In austral spring, precipitation was higher when the SPH was weaker, similar to winter. However, spring precipitation was not found to be related to SPH longitude, and higher precipitation was found when the SPH was centered farther north. In austral summer, no relationship was found between precipitation and either SPH intensity or longitude, but positive correlations were found between precipitation and latitude of the SPH. In austral autumn, correlation patterns between precipitation and all three SPH metrics more closely resembled those seen in winter. The results of a multiple linear regression confirmed the importance of two SPH metrics (intensity and longitude) and the unimportance of a third SPH metric (latitude) in understanding variability in winter, summer, and autumn precipitation in central and southern Chile. In spring, regression results confirmed a relationship between precipitation and SPH intensity and latitude. Furthermore, the SPH intensity and longitude in winter combined to hindcast monthly precipitation with a better goodness of fit than five El Niño–Southern Oscillation metrics traditionally related to Chilean precipitation. Anomalies of lower-tropospheric circulation and vertical velocities were found to support the observed relationships between SPH and precipitation. Based on these results, a physical mechanismis proposed that employs the SPHas ametric to aid in understanding variability in precipitation in central and south-central Chile in all seasons.

Warming temperatures across southeast Alaska are affecting the region’s energy and transportation sectors, marine ecosystems, and forest health. More frequent above-freezing temperatures lead a transition from snow- to rain-dominant precipitation regimes, accelerating glacial mass balance loss and a leading to a greater risk for warm-season drought. Southeast Alaska has steep topographical gradients, which necessitate the use of downscaled climate information to study historical and projected periods. This study used regional dynamical downscaling at 4-km spatial resolution with the Weather Research and Forecasting Model to assess historical (1981–2010) and projected (2031–60) climate states for southeast Alaska. These simulations were driven by one reanalysis (i.e., the Climate Forecast System Reanalysis) and two climate models (i.e., the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4), which each included a historical simulation and a projected simulation. The future simulations used the representative concentration pathway 8.5 emissions scenario. Bias-corrected projections (2031–60 minus 1981–2010) indicated seasonal warming of 1°–3°C, increased precipitation during autumn (4%–12%) and winter (7%–12%), and decreased snowfall in all seasons (up to 60% in autumn). The average number of days annually with a minimum temperature below freezing dropped by more than 30. The average annual maximum consecutive 3-day precipitation amounts increased by 11%–16%, but analogous extreme snowfall amounts dropped by 5%–11%. The most substantial snow losses occurred at low-elevation and coastal locations; at many high elevations (e.g., above 1000 m), extreme snowfall amounts increased.