Daily Precipitation Research Articles

Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China

BackgroundInfluenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work.MethodsIn order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013–2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values.ResultsTwenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013–2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20–50 mm), relative humidity (70–78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19–22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O3 concentration reached 164 µg/m3. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only.ConclusionThe extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health.

Projecting future excess deaths associated with extreme precipitation events in China under changing climate: an integrated modelling study

Climate-change-induced extreme precipitation events have attracted global attention; however, the associated excess deaths burden has been insufficiently explored and remains unclear. We first defined an extreme precipitation event for each county when the daily total precipitation exceeded the county-specific 99·5th percentile of the daily precipitation from 1986 to 2005; then we estimated the associations between extreme precipitation events and cause-specific deaths in 280 Chinese counties using a two-stage time-series model. Second, we projected the excess deaths related to extreme precipitation events by combining the bias-corrected multi-model precipitation predictions derived under different combined emission-population scenarios of three representative concentration pathways (RCPs; RCP2·6, RCP4·5, and RCP8·5) and three shared socioeconomic pathways (SSP2, a business-as-usual scenario) populations (S1, low fertility rate; S2, medium fertility rate; and S3, high fertility rate). We quantified the climate and population contributions to the changes of future excess deaths nationwide and by climatic zones. Compared with the non-extreme precipitation days, the percentage increase of deaths associated with exposure to extreme precipitation days is 13·0% (95% CI 7·0-19·3) for accidental cause, 4·3% (2·0-6·6) for circulatory disease, and 6·8% (2·8-10·9) for respiratory disease. The number of annual average excess deaths related to extreme precipitation events during 1986-2005 was 2644 (95% CI 1496-3730) for accidental cause, 69 (33-105) for circulatory disease, and 181 (79-279) for respiratory disease. In the 2030s, the total number of excess deaths of these three causes will increase by 1244 (43%), 1756 (61%), and 2008 (69%) under RCP2·6, RCP4·5, and RCP8·5 scenarios combined with a medium-fertility-rate population (SSP2-S2), respectively, but will decrease by 3% under RCP2·6-SSP2-S2 and increase by 25% under RCP8·5-SSP2-S2 in the 2090s. Humid and water-limited regions in subtropical, middle-temperate, and plateau climate zones will face highly increased risks. Climate and population factors contributed disproportionally among the five climate zones. This study is the largest integrated projection exploring the disease burden associated with extreme precipitation events. The excess deaths will be amplified by climate and population changes. Improving mitigation and adaptation capacities is crucial when responding to precipitation extremes. National Natural Science Foundation of China and Wellcome Trust.

Multi-scale assessment of high-resolution reanalysis precipitation fields over Italy

This study focuses on the validation of high-resolution regional reanalyses to understand their effectiveness in reproducing precipitation patterns over Italy, a climate change hotspot characterized by coastal sea-land interaction and complex orography. Nine reanalysis products were evaluated, with the ECMWF global reanalysis ERA5 serving as a benchmark. These included both European (COSMO-REA6, CERRA) and Italy-specific (BOLAM, MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA_IT) datasets, using different models and parametrizations. The inter-comparison involved determining the effective resolution of daily precipitation fields using wavelet techniques and assessing intense precipitation statistics through frequency distributions. In-situ observations and observational gridded datasets were used to independently validate reanalysis precipitation fields. The capability of reanalyses to depict daily precipitation patterns was assessed, highlighting a maximum radius of precipitation misplacement of about 15 km, with notably lower skills during summer. An overall overestimation of precipitation was identified in the reanalysis climatological fields over the Po Valley and the Alps, whereas multiple products showed an underestimation of precipitations across the North-West coast, the Apennines, and Southern Italy. Finally, a comparison with a time-consistent observational dataset (UniMi/ISAC-CNR) revealed a non-stable deviation from observations in the annual precipitation cumulate of the reanalysis products analyzed. This should be taken into account when interpreting precipitation trends over Italy.

Water Budget Input Linked to Atmospheric Rivers in British Columbia's Nechako River Basin

ABSTRACTThis study explores the contribution of atmospheric rivers (ARs) to the water budget input of the Nechako River Basin (NRB) in British Columbia (BC), western Canada. The study quantifies the fraction of precipitation, rainfall, snowfall, and snow water equivalent (SWE) associated with ARs at multiple scales and tests for trends using the Mann–Kendall (MK) test. AR‐related totals for 1950–2021 were created by linking AR events to water budget input variables of the ERA5‐Land reanalysis product on a daily scale. Associations with different phases of the El Niño‐Southern Oscillation (ENSO) climate pattern and AR‐related contributions to the NRB are also investigated. Results indicate an increasing fractional contribution of rain in ARs landfalling in the NRB in the last two decades (2000–2019). Moreover, 21% of the total annual precipitation in the NRB is associated with ARs, with decreasing contributions from west to east. October has higher AR‐related total precipitation than other months, while March, May and June are the least affected. ARs contribute disproportionately more to mid‐ and high‐intensity daily precipitation totals, and provide up to 45% and 24% of the seasonal rainfall and snowfall, respectively. AR‐related SWE is relatively higher in autumn due to the increased frequency and intensity of ARs, resulting in a greater fractional contribution of ARs to the snowpack compared to winter. ARs influence snowpack accumulation during fall (18%) and winter (13%) but also increase the risk of natural hazards. The MK test for AR‐related water budget variables on the annual scale identified no significant trends. However, AR‐related snowfall shows decreasing trends in the NRB, more specifically in the Upper Nechako, Lower Nechako and Stellako sub‐basins during the summer. Over the study period, ARs consistently contribute up to one‐fifth of the annual input to the NRB's water budget. This study provides the first quantitative assessment and trend analyses of AR contributions to the water budget input of a reservoir‐regulated watershed in north‐central BC, yielding valuable information for hydropower production, ecological flows, irrigation, domestic and industrial water use.

Impact of precipitation change in the Yangtze River source area on groundwater from the perspective of ground-based and satellite-based observations

In the context of global climate change, the hydrological cycle of the Yangtze River source area has produced significant changes. It is of great practical value to study the influence of regional precipitation changes and precipitation events on regional water resources, especially groundwater. This study uses base flow segmentation and GRACE groundwater reserve calculation technology to examine precipitation’s spatial and temporal variation, especially extreme precipitation events, in the Yangtze River source area. The events analyzed were the extreme precipitation-runoff-base flow response from the Yangtze River source, the extreme precipitation-terrestrial water (surface water and groundwater) response, and the comparison of these events. The findings indicated that extremely strong precipitation, maximum precipitation for five consecutive days, days of moderate rain and above moderate rain intensity precipitation, the longest number of precipitation days, and other extreme precipitation indices had a significantly increasing trend and increased intensity of extreme precipitation events, the regional differences in precipitation trended less significantly. The base flow index gradually increased at a rate of 0.0014 yr-1, suggesting the overall increase of water resources and the contribution rate of groundwater to the runoff was an increased trend . There is a high correlation between the change of water resources in the source area and the change of precipitation, and the increased precipitation influenced an increase in groundwater and regional water resources. These findings inform current knowledge of precipitation-groundwater response mechanisms in alpine and arid regions such as the Qinghai-Tibet Plateau.

A high-resolution dataset for future compound hot-dry events under climate change.

Global climate change is leading to an increase in compound hot-dry events, significantly impacting human habitats. Analysing the causes and effects of these events requires precise data, yet most meteorological data focus on variables rather than extremes, which hinders relevant research. A daily compound hot-dry events (CHDEs) dataset was developed from 1980 to 2100 under various socioeconomic scenarios, using the latest NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset to address this. The dataset has a spatial resolution of 0.25 degrees (approximately 30 kilometres), including three indicators, namely D (the yearly sum of hot-dry extreme days), prI (the intensity of daily precipitation), and tasI (the intensity of daily temperature). To validate the accuracy of the dataset, we compared observational data from China (National Meteorological Information Center, NMIC), Europe (ERA5), and North America (ERA5). Results show close alignment with estimated values from the observational daily dataset, both temporally and spatially. The predictive interval (PI) pass rates for the CHDEs dataset exhibit notably high values. For a 90% PI, D has a pass rate exceeding 85%, whilst prI and tasI respectively show a pass rate above 70% and 95%. These results underscore its suitability for conducting global and regional studies about compound hot-dry events.

A dataset of high-resolution climate change projections over South America with bias correction

Accurate and detailed datasets are crucial for assessing climate change impacts. Regional climate models provide high-resolution simulations and are key tools but often exhibit systematic biases. Therefore, this paper presents a dataset derived from bias-corrected Eta regional model simulations and projections driven by four global CMIP5 models. The correction applied to daily precipitation, potential evapotranspiration, actual evapotranspiration, and 2-m air temperature, was conducted on a 0.2° x 0.2° grid over South America. The dataset covers two periods: 1976-2005 (baseline) and 2006-2099 (future) under RCP4.5 and RCP8.5 scenarios. Empirical quantile mapping was used to adjust the Eta model outputs to better match observational data. This method modified the accumulated probability curves of the Eta model outputs to align with observational curves for both baseline and future climates. Two observational datasets were used for correction and evaluation. The new dataset shows that bias correction significantly reduced the errors in the Eta simulations, especially for the frequent values of precipitation, potential evapotranspiration, and 2-m temperature, and also corrected the annual cycle and frequency distribution of these variables, approaching the observations. The pattern of extreme precipitation indices from the bias-corrected Eta dataset also reduced error. Bias correction was applied to future projections. The comparison against the raw Eta dataset showed that the trends of changes were preserved, but in general, the peaks of the changes were smoothed. As in the raw Eta dataset, the RCP8.5 scenario showed a higher change rate than RCP4.5. This work also revealed the large uncertainty of the observational dataset; some of the remaining errors after the bias correction were mostly due to differences between the two correction and evaluation observational datasets. The described dataset is freely available from the CNPq LattesData repository at the following link: https://doi.org/10.57810/lattesdata/WAVGSL.

Bias correction of daily precipitation from climate models, using the Q‐GAM method

AbstractClimate models are useful tools for analyzing historical and projecting future climate conditions. However, the model results tend to differ systematically from observations, particularly for parameters with complex spatial and temporal distributions such as precipitation. A combination of quantile mapping and generalized additive models (GAMs) is presented and proposed as a new method (Q‐GAM) for the bias correction of daily precipitation. Q‐GAM is demonstrated by using data from five European stations with different climate characteristics. For each station, the closest continental grid point of a EURO‐CORDEX climate model was selected for bias correction. A bootstrapping experiment is presented with over 1000 repetitions of randomly splitting the historical period 1981 to 2005 into a calibration and evaluation period. The results for all stations reveal that Q‐GAM is a straightforward, accurate and computationally efficient method for the bias correction of precipitation. In particular, the method improves the frequency of dry days and the total annual rainfall amount. This outcome is robust across stations with varying climate characteristics and also to the choice of calibration and evaluation periods. Similar results are also obtained for other precipitation characteristics such as the 0.9 and 0.95 quantiles.

The asymmetric distribution of rainfall frequency and amounts in India

Studies of rainfall usually focus on the total amount precipitating throughout a certain period. Compared to rain rates associated with extreme events, the rain rates associated with the most frequent events is understudied. In this study, the characteristics of daily precipitation in India are explored using two metrics − rain frequency peak (the most frequent non-zero rain rate) and rain amount peak (the rain rate at which the most amount of rain falls). These metrics are computed over India using local and global datasets to investigate the characteristics of typical daily precipitation accumulations. These values are sensitive to the dataset used for this analysis since the temporal and spatial resolution of the rainfall data will influence the rain frequency peak and rain amount peak. Our study reveals the rain frequency peak is highest during the summer monsoon, while the winter season exhibits lower values, particularly at higher latitudes. Similarly, the rain amount distribution indicates dominance of heavy rain rates during the monsoon and post-monsoon seasons, leading to high total precipitation. The maximum rain frequency peak for any region in India reaches up to a value of 35 mm/day while the maximum rain amount peak reaches up to a value as high as 90 mm/day. These metrics would be useful in systematically evaluating typical daily precipitation in regional climate models and assessing downstream impacts of uneven precipitation such as lower crop yields, flood-drought alterations, and fluctuating water availability.

Relationships between Precipitation and Elevation in the Southeastern Tibetan Plateau during the Active Phase of the Indian Monsoon

The precipitation gradient (PG) is a crucial parameter for watershed hydrological models. Analysis of daily precipitation and elevation data from 30 stations in the southeastern Tibetan Plateau (SETP) during the active phase of the Indian monsoon reveals distinct patterns. Below 3000 m, precipitation generally decreases with increasing altitude. Between 3000 and 4000 m, precipitation patterns are more complex; in western regions, precipitation increases with elevation, whereas in eastern regions, it decreases. Above 4000 m, up to the highest observation point of 4841 m, precipitation continues to decrease with elevation, with a more pronounced decline beyond a critical height. In the SETP, PGs for LYR and NYR are positive, at 11.3 ± 2.7 mm/100 m and 17.3 ± 3.8 mm/100 m, respectively. Conversely, PLZB exhibits a negative PG of −22.3 ± 4.2 mm/100 m. The Yarlung Zangbo River (YLZBR) water vapor channel plays a significant role in these PGs, with the direction and flux of water vapor potentially influencing both the direction and magnitude of the PG. Additional factors such as precipitation intensity, the number of precipitation days, precipitation frequency, and station selection also significantly impact the PG. Notable correlations between elevation and variables such as the number of precipitation days, non-precipitation days, and precipitation intensity. The precipitation intensity gradients (PIGs) are 0.06 ± 0.02 mm/d/100 m, 0.11 ± 0.04 mm/d/100 m, and −0.18 ± 0.04 mm/d/100 m for the three catchments, respectively. Future research should incorporate remote sensing data and expand site networks, particularly in regions above 5000 m, to enhance the accuracy of precipitation–elevation relationship assessments, providing more reliable data for water resource simulation and disaster warning.

Spatiotemporal characteristics of daily precipitation concentration in Iran

Spatiotemporal characteristics of daily precipitation concentration in Iran

Variations and Trends in 115 Years of Graded Daily Precipitation Records at Three Hydrometeorological Stations in Finland

This study investigated the variability and trends in 115 years (1909–2023) of daily precipitation observed at three hydrometeorological stations in southern (Kaisaniemi), central (Kajaani), and northern (Sodankylä) Finland. We also identified the most significant climate teleconnections influencing daily precipitation variability at these three stations during the period 1951–2023. The daily precipitation records were primarily classified into six grades, including very light (≤1 mm), light (1–≤5 mm), moderate (5–≤10 mm), heavy (10–≤15 mm), very heavy (15–≤20 mm), and extreme (&gt;20 mm). On average, the most intense daily precipitation was determined at the Kaisaniemi station in southern Finland. At this station, however, very light and light precipitation showed the lowest frequency, but other graded daily precipitation events were the most frequent. At all three stations, the intensity of very light precipitation significantly declined during the past 115 years, while its frequency increased. The highest rates of such decreases and increases in the intensity and frequency of very light daily precipitation were found at the Sodankylä stations in northern Finland, respectively, but the lowest rates were at the Kaisaniemi station in the south. At the Kajaani station in central Finland, the intensity of light precipitation decreased, but very heavy precipitation intensified. At this station, however, the number of both moderate and heavy precipitation events increased over time. Finally, historical variations in both the intensity and frequency of graded daily precipitation events in Finland showed significant relationships with different climate teleconnections, particularly the Scandinavia (SCAND) and the North Atlantic Oscillation (NAO) patterns.

Stable-isotope variability in daily precipitation: insights from a low-cost collector in SE England

ABSTRACT Precipitation stable-isotope data are often used in hydroclimatic, hydrological and hydrogeological investigations, with measurements typically undertaken on integrated monthly samples. However, daily sampling reveals overlooked aspects of controls on precipitation isotope values, including synoptic meteorological conditions. We present a one-year record of stable isotopes in daily precipitation during 2021, from a site in SE England close to Greater London. We find marked daily variability over the course of the year (–15.62 to +0.92 ‰ for δ18O, –108.7 to +2.9 ‰ for δ2H and –6.5 to +23.1 ‰ for deuterium excess). Correlations with individual meteorological variables including precipitation amount, temperature and weather type are moderate to weak suggesting complex controls on the daily rainfall isotope values. The daily data are compared with three other daily datasets from England and, by conversion to monthly values, directly with data from three long-term collection stations across Britain and Ireland. The scale of variability in the daily data from our site is consistent with that seen in other English records despite them all coming from different time periods. The monthly data show broad consistency, although there are differences that also highlight geographical variability in precipitation values across the British Isles.

Breaking Rossby waves drive extreme precipitation in the world’s arid regions

More than a third of the world’s population lives in drylands and is disproportionately at risk from hydrometeorological hazards such as drought and flooding. While weather systems governing precipitation formation in humid regions have been widely explored, our understanding of the atmospheric processes generating precipitation in arid regions remains fragmented at best. Here we show, using a variety of precipitation datasets, that Rossby wave breaking is a key atmospheric driver of precipitation in arid regions worldwide. Rossby wave breaking contributes up to 90% of daily precipitation extremes and up to 80% of total precipitation amounts in arid regions equatorward and downstream of the midlatitude storm tracks. The relevance of Rossby wave breaking for precipitation increases with increasing land aridity. Contributions of wave breaking to precipitation dominate in the poleward and westward portions of arid subtropical regions during the cool season. Our findings imply that Rossby wave breaking plays a crucial role in projections and uncertainties of future precipitation changes in societally vulnerable regions that are exposed to both freshwater shortages and flood hazards.

The Spatiotemporal Evolution of Extreme Climate Indices in the Songnen Plain and Its Impact on Maize Yield

Global climate change is intensifying and extreme weather events are occurring frequently, with far-reaching impacts on agricultural production. The Songnen Plain, as an important maize production region in China, faces challenges posed by climate change. This study aims to explore the effects of climate extremes on maize yield and provide a scientific basis for the adaptation of agriculture to climate change in this region. The study focuses on the spatial and temporal evolution characteristics of climate extremes during the maize reproductive period from 1988 to 2020 in the Songnen Plain and their impacts on maize yield. Daily temperature and precipitation data from 11 meteorological stations were selected and combined with maize yield information to assess the spatial and temporal trends of extreme climate indices using statistical methods such as the moving average and Mann–Kendall (M-K) mutation test. Pearson correlation analysis and a random forest algorithm were also used to quantify the degree of influence of extreme climate on maize yield. The results showed that (1) the extreme heat and humidity indices (TN90p, TX90p, CWD, R95p, R10, and SDII) tended to increase, while the cold indices (TN10p, TX10p) and the drought indices (CDD) showed a decreasing trend, suggesting that the climate of the Songnen Plain region tends to be warmer and more humid. (2) The cold indices in the extreme temperature indices showed a spatial pattern of being higher in the north and lower in the south and lower in the west and higher in the east, while the warm indices were the opposite, and the extreme precipitation indices showed a spatial pattern of being higher in the east and lower in the west. (3). Both maize yield and trend yield showed a significant upward trend. Maize meteorological yield showed a fluctuating downward trend within the range of −1.64~0.79 t/hm2. During the 33 years, there were three climatic abundance years, two climatic failure years, and the rest of the years were normal years. (4) The cold index TN10p and warm indices TN90p and CWD were significantly correlated with maize yield, in which TN90p had the highest degree of positive correlation with yield, and in the comprehensive analysis, the importance of extreme climatic events on maize yield was in the order of TN90p, TN10p, and CWD. This study demonstrates the impact of extreme climate indices on maize yield in the Songnen Plain, providing a scientific basis for local agricultural management and decision-making, helping to formulate response strategies to mitigate the negative effects of extreme climate, ensure food security, and promote sustainable agricultural development.

Multimodel regional frequency analysis of CMIP extreme precipitation

Abstract A recurrent question in climate risk analysis is determining how climate change will affect&amp;#xD;heavy precipitation patterns.&amp;#xD;Dividing the globe into homogeneous sub-regions should improve the modeling of heavy precipitation by inferring common regional distributional parameters.&amp;#xD;In addition, biases due to model errors in global climate models (GCMs) should be considered to understand the climate response to diffrent forcing effects.&amp;#xD;Within this context, we propose an efficient clustering algorithm that, compared to classical regional frequency analysis (RFA) techniques, is covariate-free and accounts for dependence.&amp;#xD;It is based on a new non-parametric dissimilarity that combines both the RFA constraint and the pairwise dependence.&amp;#xD;We derive asymptotic properties of our dissimilarity estimator, and we interpret it for generalized extreme value distributed pairs.&amp;#xD;&amp;#xD;As an application, we cluster annual daily precipitation maxima of 16 GCMs from the coupled model intercomparison project.&amp;#xD;We combine the climatologically consistent subregions identified for all GCMs.&amp;#xD;This improves the spatial clusters coherence and outperforms methods either based on margins or on dependence.&amp;#xD;Finally, by comparing the natural forcings partition with the one with all forcings, we assess the impact of anthropogenic forcing on precipitation extreme patterns.

Spatiotemporal changes in tourism climate comfort in Sichuan Province from 1961 to 2022

AbstractBased on the daily temperature, wind speed, relative humidity, sunshine hours and precipitation in Sichuan Province from 1961 to 2022, the spatiotemporal variation characteristics of tourism climate comfort in Sichuan Province were analysed using the temperature–humidity index (THI), precipitation index (P), sunshine duration index (SSD), wind chill index (WCI) and tourism climate index (TCI). The results show that the average annual TCI in Sichuan Province over the past 62 years is “acceptable” or above and shows a significant increasing trend at a rate of 0.018 per decade. On average, the best travel comfort period in Sichuan Province is from March to May and from October to November. The TCI increased significantly in cold seasons such as February and November with rates of 0.064 and 0.053 per decade, respectively. The increase in TCI is mainly controlled by the increase in THI and WCI, both of which are related to the increase in temperature, with a greater effect in cold seasons and at higher elevations. Most of China's 5A‐level tourist attractions in Sichuan Province (14 of 17) have an annual TCI of “acceptable” or better. Similarly, the top 3 months for climate comfort at these attractions tend to be March to May and October, with ratings reaching “good” or even “very good.” For the other three destinations, all characterized by glaciers and mountains, the top 3 months for climate comfort are concentrated in the warm months. The overall tourism climate comfort of Sichuan Province is increasing, and the climate is changing in a positive way for the tourism industry.

Spatial and temporal analysis and trends of extreme precipitation over the Mississippi River Basin, USA during 1988–2017

Study regionMississippi River Basin. Study focusUsing daily precipitation records of 769 meteorological stations over the Mississippi River Basin (MRB), the spatial-temporal variability and trend of nine extreme precipitation indices were estimated and statistically assessed using the Mann-Kendall test. Factors likely to influence the spatial pattern and trends of precipitation extremes indices were also checked. New hydrological insights for the regionThe spatial pattern of the extreme precipitation indices exhibits a southeast to Northwest dipole, with the maximum values recorded over the southeastern part of the domain (exception being for Consecutive Dry Days, CDD which shows otherwise) driven by the southerly moisture transport toward the southeast. The spatial pattern of the extreme precipitation is controlled by the topography. The results also show that, on average, almost all the indices (except CDD) exhibit an increasing trend. The total wet day precipitation exhibits a significant increasing trend. Spatially, most of the significant increasing (decreasing) trends of the extreme's precipitation-except CDD- are located over the Upper (South) MRB where there is a significant sign toward cooling (warming) conditions. This supports the view that changing climate towards warming (cooling) conditions is significantly affecting precipitations extremes over the MRB. The relationships between large-scale teleconnections and extreme precipitation show that Pacific North America significantly increases (decreases) frequency and intensity indices over the Northwest (southeast) MRB, whereas the Pacific Decadal Oscillation does increase the frequency and intensity indices over the southeast. El Niño Southern Oscillation significantly increases the frequency and intensity indices over the entire MRB, with consequences to infrastructure failures, increasing vulnerable populations, risk zones and relocations populations.

Multivariate bias correction and downscaling of climate models with trend-preserving deep learning

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

Spatial and Temporal Variations’ Characteristics of Extreme Precipitation and Temperature in Jialing River Basin—Implications of Atmospheric Large-Scale Circulation Patterns

In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should be more deeply explored for its patterns and associations with climatic factors. In this study, based on the daily precipitation and atmospheric temperature datasets from 29 meteorological stations in JRB and its vicinity from 1960 to 2020, 10 extreme indices (6 extreme precipitation indices and 4 extreme temperature indices) were calculated. The spatial and temporal variations of extreme precipitation and atmospheric temperature were analyzed using Mann–Kendall analysis, to explore the correlation between the atmospheric circulation patterns and extreme indices from linear and nonlinear perspectives via Pearson correlation analysis and wavelet coherence analysis (WTC), respectively. Results revealed that among the six selected extreme precipitation indices, the Continuous Dry Days (CDD) and Continuous Wetness Days (CWD) showed a decreasing trend, and the extreme precipitation tended to be shorter in calendar time, while the other four extreme precipitation indices showed an increasing trend, and the intensity of precipitation and rainfall in the JRB were frequent. As for the four extreme temperature indices, except for TN10p, which showed a significant decreasing trend, the other three indices showed a significant increasing trend, and the number of low-temperature days in JRB decreased significantly, the duration of high temperature increased, and the basin was warming continuously. Spatially, the spatial variation of extreme precipitation indices is more obvious, with decreasing stations mostly located in the western and northern regions, and increasing stations mostly located in the southern and northeastern regions, which makes the precipitation more regionalized. Linearly, most of the stations in the extreme atmospheric temperature index, except TN10p, show an increasing trend and the significance is more obvious. Except for the Southern Oscillation Index (SOI), other atmospheric circulation patterns have linear correlations with the extreme indices, and the Arctic Oscillation (AO) has the strongest significance with the CDD. Nonlinearly, NINO3.4, Pacific Decadal Oscillation (PDO), and SOI are not the main circulation patterns dominating the changes of TN90p, and average daily precipitation intensity (SDII), maximum daily precipitation amount (RX1day), and maximum precipitation in 5 days (Rx5day) were most clearly associated with atmospheric circulation patterns. This also confirms that atmospheric circulation patterns and climate tend not to have a single linear relationship, but are governed by more complex response mechanisms. This study aims to help the relevant decision-making authorities to cope with the more frequent extreme climate events in JRB, and also provides a reference for predicting flood, drought and waterlogging risks.