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We characterize trends in maximum seasonal daily precipitation (seasonal Rx1day), minimum (Tn), and maximum (Tx) daily temperatures during days with precipitation over continental Chile for the period 1979–2017, using surface stations and the AgERA5 gridded product derived from the ERA5 reanalysis dataset. We also examine seasonal trends of Sea Surface Temperature (SST), Precipitable Water (PW), Convective Available Potential Energy (CAPE), Eddy Kinetic Energy (EKE), Atmospheric Rivers (ARs) frequency, and upper air observations to seek possible mechanisms that explain precipitation trends. Our results show an increase in seasonal Rx1day during fall in the south part of Northern Chile (15–30°S) and during fall and winter in Austral Chile (45–57°S), and mostly negative trends in Central Chile (30–36°S), where a few locations with positive trends along the coast during summer. Temperature trends presented cooling patterns north of 33°S in almost all the seasons (< -2 °C/dec), while warming trends prevail south of 38°S (> 1 °C/dec). The highest values in Tn trends are obtained on the western slopes of the Andes around 30°S. We also explore temperature scaling in surface stations, finding strong positive super Clausius Clapeyron with Tn, especially between fall and spring in the 33–40°S region. Sounding observations in five stations across Chile suggest warming trends at 23.5°, 33°S, and 53°S, with a stabilization effect by enhanced warming in the upper troposphere, while presenting cooling trends in Puerto Montt (41.5°S). Seasonal trends in PW reveal moistening along southern Peru and northern Chile during spring and summer. Positive trends in CAPE are observed over 35–40°S (austral summer and fall) and the north Altiplano (autumn). SST analyses reveal strong cooling around 30°S in winter, which may explain the negative trends in seasonal Rx1day in central Chile. A warming spot on the northern Peruvian coast during fall may be responsible for humidification in front of Northern Chile, particularly during summer and fall. Positive EKE trends are detected south of 40°S, being stronger and reaching almost all of the coast during spring. ARs frequency unveils negative trends up to -5 days/dec during summer and positive trends of 1 day/dec in 40°- 50°S coastal regions during spring. More generally, the results presented here shed light on the main large-scale processes driving recent trends in precipitation extremes across continental Chile.

We analyze annual extremes of daily maximum and minimum surface air temperature and of daily rainfall in East Asia and the Korean peninsula. This study made intensive use of the simulation data available from the CMIP5 (Coupled Model Intercomparison Project Phase 5) multimodels in historical and future experiments up to the year 2100, employing three different radiative forcings: RCP2.6, RCP4.5, and RCP8.5 (representative concentration pathways). Several reanalysis datasets are used to compare and evaluate the simulated climate extremes in the late twentieth century. We estimate the future changes in precipitation and temperature extremes in East Asia and Korea, and compare them to the global result, for the reference period 1986–2005. The rising rate of future cold extremes over East Asia and Korea is faster than that of warm extremes. This phenomenon appears more distinctly in Korea as a local scale, indicating more sensitivity of the Korean peninsula to global warming. The increase of the 20-year return level of maximum precipitation in the CMIP5 over East Asia by the end of twenty-first century is about 7% in the RCP2.6, 15% in the RCP4.5, and 35% in the RCP8.5 experiments, which exceed the corresponding global values. We also estimate the changes in precipitation extremes across East Asia as a function of the annual mean temperature variation at the same location. The CMIP5 sensitivity in maximum precipitation across East Asia is 5.5%/∘C, which is lower than the global figure (5.8%/∘C). The sensitivity for the Korean peninsula is 7.38%/∘C, indicating the strong impact of global warming to Korea. The results will be important in mitigating the detrimental effects of variations of climatic extremes and in improving the regional strategy for water resource and eco-environmental management, particularly for such areas in East Asia under significant changes in temperature and rainfall extremes.

Characterizing extreme precipitation precisely is crucial for predicting vegetation response to drought or storms. However, current precipitation generators in vegetation models do not simulate the occurrence and amount of extreme precipitation well. This study examined the effects of extreme precipitation on the skewness, kurtosis, and skewness–kurtosis interaction of annual precipitation distribution. The examination was based on theoretical calculations and monitoring data from 78 meteorological stations on the Qinghai–Tibetan Plateau (QTP). The results showed that extreme precipitation generally increased the skewness and kurtosis of annual precipitation distribution. A higher mean annual precipitation amplified the effects of precipitation extremes on promoting skewness and kurtosis in normal distribution scenarios. In contrast, these effects tended to be saturated for scenarios of higher mean annual precipitation in probability-based distributions. A reduction of dry days in a year markedly intensified the interaction of the skewness and the kurtosis, while the skewness–kurtosis interaction weakened with decreased maximum daily precipitation in a year. Moreover, the effect of extreme precipitation on the skewness–kurtosis interaction was stronger in arid or low-altitude areas. This study illustrates the fact that considering the skewness and kurtosis of annual precipitation distributions will be very helpful for simulating extreme precipitation on the QTP in the future. This will allow us to better understand the impact of climate change on alpine plants.

This article studies the intra-annual and long-term variability in the maximum daily precipitation totals and their association with atmospheric circulation in Kraków. It investigates daily precipitation maxima by year and by month. The research is based on daily precipitation totals in the years 1863–2021 and draws on the calendar of atmospheric circulation types by Niedźwiedź. It examines the frequency of precipitation maxima in individual months and their variation from one year to another. No statistically significant trend of change in precipitation over the study period has been found. All annual maximum daily precipitation totals in Kraków fall into the category of heavy precipitation (>10 mm), and almost 99% qualify as very heavy (>20 mm). In the summer months, these are about 3–4 times higher than in winter. The share of the daily precipitation maximum in the monthly total exceeds 30% in all months. The maximum daily precipitation occurring on 5 August 2021 was the highest in the period that extends from the start of instrumental measurements. The study period saw 12 cases of maximum precipitation that belong to ‘flood-inducing’ categories (over 70 mm/day). Such cases of the very heaviest precipitation occurred in cyclonic situations: Cc, Bc, Nc, NEc, Ec and SEc. Most spring and summer maxima were seen on days with a cyclonic circulation. The instances of high daily precipitation in the Kraków area led to the flooding of residential and historic buildings, as well as of municipal infrastructure.

Changes in climate extremes have become a hot issue in the research field of climate change recently. Many studies have reported that climate extremes have occurred more frequently and with increasing intensity in recent decades. In this study, thresholds of precipitation and drought extremes were determined by the cumulative distribution function, and their spatiotemporal changes over the past half-century in China were analyzed by relative change rate. The results show that: (1) precipitation extremes increased in all regions except North China, while increasing trends of drought extremes were detected in all regions except Northwest China and the Qing–Tibet Plateau; (2) the maximum change rates in frequency of precipitation extremes were found in Northwest China and the Qing–Tibet Plateau, with values of 16.13% and 8.12%, and the maximum change rates in frequency of drought extremes were in Southwest and Southeast China, whose increases in intensity of drought extremes were also the maximum; (3) variation in precipitation extremes showed a relatively mixed pattern with higher heterogeneity compared to that of drought extremes; and (4) changes in precipitation and drought extremes relate to mid-intensity, lower-intensity, and annual precipitation.

High-intensity rainfall can raise fluvial channel levels, increasing the risk of flooding. Maximum precipitation depths are used to estimate return periods and, thus, calculate the risk of this type of event. To improve these estimates in Southwest Europe, we studied the behavior of extreme rainfall using the historical records of San Fernando (Cádiz, southwest Spain), obtaining the maximum daily annual rainfall (period 1851–2021). Local risk levels for intense precipitation were established based on the mean values and standard deviation of daily precipitation. In this series, 38% of the years had some type of risk (>53.7 mm), of which 13% of these years had high risk (>73.2 mm) or disaster risk (>92.7 mm). In these risk thresholds, the maximum daily precipitation is mostly concentrated in the autumn months. The SQRT-ETMax model used fits well with the instrumental historical records for return periods of up to 25 years, although it may present appreciable deviations for longer return periods. Using a 170-year secular series, a more precise understanding of extreme periods and precipitation variability was obtained.

Variability and change of precipitation were investigated in Kumamoto on Kyushu Island in southwestern Japan, to assess water resources and flooding risk. Annual precipitation, annual maximum daily precipitation, and annual maximum hourly precipitation have increased over the period from 1891 to 2018 (128 years). Trends are 26.2 mm per decade, 6.07 mm/day per decade, and 2.17 mm/h/decade, respectively. Precipitation in the rainy season (June and July) is on average 37% (ranging from 12 to 59%) of annual precipitation for the 128-year period. Maximum daily precipitation in a year occurred at Kumamoto in the rainy season in 92/128 (72%) of the years of observation from 1891 to 2018, in the typhoon (August to November) season in 23/128 (18%), and in the March to May season in 12/128 (10%). This indicates that the rainy monsoon season poses the largest daily flooding risk. A wavelet analysis revealed that from 1891 to 2018 annual precipitation and daily maximum precipitation fluctuate with 2 and 4 years periods, which may be related to the El Nino-Southern Oscillation (ENSO). It is likely that air temperature rises, ENSO and topographical characteristics contributed to an increase in precipitation in the period. The analysis also showed that typhoons hitting or approaching Kumamoto have significantly affected annual precipitation and annual maximum daily precipitation, while the interval between typhoons affecting Kumamoto has been getting longer since the 1970s.

Hydrological frequency analysis plays an indispensable role in the construction of national flood control projects. This study selects the stations with the smallest and largest discordances in the nine homogeneous regions of Sichuan Province as the representative stations, and results obtained by regional frequency analysis are compared with those obtained by traditional at-site hydrological frequency analysis. The results showed that the optimal frequency distribution of each representative station obtained by traditional at-site hydrological frequency analysis and the ones of corresponding homogeneous regions obtained by regional frequency analysis were not necessarily consistent, which was related to the site and homogeneous regions. At the same time, there were also differences between the fitting of the theoretical rainstorm frequency curve obtained by the two methods and the observation. In general, in each homogeneous region, the results obtained by regional frequency analysis and traditional at-site hydrological frequency analysis at the stations with the largest frequency analysis were quite different. The design values obtained by the two methods were also increasingly different with the increase of the return period. The study has specific reflections on the differences between regional frequency analysis and traditional at-site hydrological frequency analysis.

Zhou, Z.; Liu, S.; Hua H.; Chen, C.S.; Zhong G.; Lin, H., and Huang, C.W., 2014. Frequency analysis for predicting extreme precipitation in Changxing station of Taihu Basin, China. Rainfall induced flooding is one of the most severe natural disasters in coastal regions. In recent years, along with global warming and sea level rising, extreme hydrological events, such as extreme precipitation, are of high occurrence. Meanwhile, rapid urbanization makes the urban environment transformed dramatically and results in additional flood ventures. Taihu Basin, located in Yangtze Delta, is the richest basin in China and flood control planning in this region is of high significance. Therefore, precipitation analysis, as a basic work of flood design, should be accurate and precise. In this study, based on precipitation data at Changxing Station of Taihu Basin, precipitation frequency analysis using mixed methods is performed. Two of the most applied distribution models, Pearson-III (P3) and Generalized Extreme Value (GEV), are investigated. For parameter estimation method of probability distribution functions, maximum likelihood estimation (MLE) and L-moments (LM) are used. In addition, seeking-matrix curve fitting based on conventional moments (CM) is also investigated to compare the calculation results. The performance of mixed methods is tested by two classical goodness-of-fit tests, Chi-Square test and K-S test. Consequently, GEV distribution model based on LM is evaluated to be the best fitting model for identifying and predicting future precipitation occurrence. So precipitation estimations from different return periods at Changxing Station are identified. This study is a new attempt to precipitation frequency analysis in the stations of Taihu Basin and the result can provide a reference for flood risk and water resource management in Taihu Basin and even in more other regions in China.

The precipitation of General Circulation Model (GCM) output for Han River Basin, Korea was downscaled into a regional watershed using the Artificial Neural Network (ANN) model with multiple statistical processors of Nonstationary Quantile Mapping (NSQM) and a Stochastic Typhoon Model (STM). The stochastically generated typhoon rainfall was synthesized and added to the ANN-processed precipitation, and the summer precipitation underestimated by raw GCM was effectively recovered. The projection was evaluated in terms of the annual and seasonal quantities and annual daily maximum precipitation. The spatial dependency structure of the projection results were tested through the spatial autocorrelation indices of Moran’s I and LISA (Local Indicators of Spatial Association). The results under baseline, B1 and A1B scenarios represent the effective reproduction of spatial autocorrelation, even though the hot spots of DJF under the A2 scenario shifted to an adjacent area. The seasonal analysis shows that the GCM output in JJA is relatively more reliable than other seasons in view of baseline biases (8.6%) and the conservation of spatial autocorrelation (Moran’s I = 0.63). Under the 95% level of confidence, the annual precipitation by the year 2040 is projected to be 3.2%, 7.7% and 10.8% increase with respect to the baseline period under B1, A1B and A2 scenarios respectively. Under the same level of confidence, the JJA seasonal projection gives a −1.7%, 3.2% decrease and 7.5% increase under B1, A1B, and A2 scenarios, respectively. The rate of increase of JJA precipitation will be less than the annual total. Meanwhile the increasing ratio (13.2% mean increase for all scenarios) of the daily maximum precipitation is obviously higher than the annual or seasonal total precipitation, which means the impacts on hydrological extremes due to climate change would be more intense than one can sense.