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By observing well‐known sea surface temperature (SST) indices and outputs from Climate Forecast System version 2 (CFSv2) hindcasts and predictions, in this study, we developed a hybrid statistical downscaling prediction model (HSDPM) based on a timescale decomposition approach, which effectively improved the forecasting ability of summer (June–August) precipitation over Hainan Island (HNSP) in China; this precipitation presented multiple obvious timescale variabilities. We found that the interannual variability of the HNSP is closely related to the observed North Atlantic tripole SST anomalies in the preceding winter and the summer sea level pressure over the South China Sea by CFSv2 predicted in March. On the other hand, the interdecadal variability in the HNSP is linked to the prewinter Pacific Interdecadal Oscillation (PDO) according to the observations, the CFSv2 predictions of the summer surface air temperature over the tropical central‐east Pacific. On this basis, both the interannual and interdecadal components of the HNSP are effectively predicted using the corresponding predictors via multiple regression; thus, the HSDPM is ultimately established by combining the above two components. Compared with the original CFSv2 model, the HSDPM model achieves a considerable improvement in performance in predicting the HNSP. Specifically, the temporal correlation coefficient, ratio of root‐mean‐square error and anomaly sign consistency rate between the observed and HSDPM‐predicted HNSPs are 0.70, 17.4% and 80.5%, respectively, which are significantly greater than the above three forecasting index values of 0.23, 38.3% and 48.8%, respectively, obtained from the original CFSv2 predictions. The application of the HSDPM may be beneficial for drought and flood prevention and mitigation in Hainan Island. [ABSTRACT FROM AUTHOR]

To evaluate future changes in the climate system, the outputs from coarse‐resolution global climate models (GCMs) need to be downscaled to a finer scale, making them more directly applicable for impact assessment. Here we focus on examining the projected changes of three key climate variables (precipitation, air temperature, and wet bulb temperature) across Arizona (south‐western United States). We use daily outputs from GCMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and bias correct and downscale them to a 4‐km resolution. Through leave‐one‐out cross‐validation, we compare various bias correction methods and identify that the empirical quantile mapping approach performs the best regardless of the climate variable. Then, we analyse the projected bias‐corrected outputs for two future periods (Mid‐of‐Century: 2015–2048; End‐of‐Century: 2067–2100) with respect to the 1981–2014 period, under four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). Our results show that Arizona's climate is projected to become overall warmer and wetter, more so towards the end of this century and for higher emission scenarios. Additionally, our findings project an increase in wet bulb temperature and cooling degree days, implying the ongoing warming climate's potential impacts on public health and the economy. These results provide a baseline understanding of climate change impacts in the state and highlight the projected changes in the future in response to the climate scenarios. [ABSTRACT FROM AUTHOR]

The seasonal cycle (SC) of surface air temperature is a substantial element in climatology. Some basic and important topics in climate change studies are based on accurate and reliable estimation of SCs, such as percentile‐based indices of extremes and probability density function (PDF) changes of daily temperatures. For both of them, most studies characterized SCs using averages of multi‐days windows, which is not smooth and accurate enough representing the extreme thresholds and climatological normal of SCs. It is necessary to construct smooth and reasonable SCs for more accurate estimation of temperature changes on extreme thresholds and PDFs. In this study, we propose a flexible method based on generalized additive models for location scale and shape and penalized b‐spline smoothing technique with respective distributions to construct smooth SCs and SC for extreme temperatures (SCETs). The accuracy of the constructed smooth SCETs is good with the estimation biases of percentiles tending to be zero. The constructed smooth SCET also exhibits good stability over time, such that the magnitude changes of temperatures on each calendar day are close to climatic changes of mean temperature when the concerned period shifts. Based on the constructed smooth SCs, climatic changes by seasons and PDFs between two periods, 1961–1990 and 1991–2020, are examined over China. The increase of thresholds for hot extremes in spring during the recent period is prominent, while the increase of thresholds for daytime cold extremes in summer over a part of central to southern China is also notable. The smooth SCs and SCETs based on our flexible statistical modelling framework can characterize daily extreme temperatures reasonably and accurately, and should be expected to have more applications for a better understanding of climate changes related to distributions and seasonal cycles. [ABSTRACT FROM AUTHOR]

The purpose of this research is to identify changes in the spatiotemporal distribution and severity of meteorological droughts in Ukraine between 1946 and 2020. In order to determine the role of precipitation and atmospheric evaporative demand on drought severity, two drought indices were applied for comparative analysis: the standardized precipitation index (SPI) and the standardized evaporation precipitation index (SPEI). Both indices were calculated on 3‐ and 12‐month timescales. The gridded dataset of monthly air temperature and atmospheric precipitation for Ukraine with high spatial resolution (0.1° × 0.1°), developed in the Ukrainian Hydrometeorological Institute, was used. Results of time series analysis of drought indices showed at least 20 episodes, in which more than 25% of the territory of Ukraine was affected by drought of varying intensity. Trends show an increase in drought severity determined mostly by a strong observed increase in the atmospheric evaporative demand. Tendencies towards an increase in the duration and intensity of droughts were observed mainly in the southwestern, central and northern regions of Ukraine. The main cause of the increase was droughts in spring and summer. The temporal analysis of the spatial distribution of areas affected by drought during the identified main drought episodes showed different patterns in drought propagation across the territory of Ukraine. [ABSTRACT FROM AUTHOR]

Under global warming, the summer surface air temperature (SAT) change signal in the northern mid‐latitudes is generally the most significant, while the SAT change in North China (NC) shows distinct local characteristics. Using simulations from the Coupled Model Intercomparison Project Phase 6 and observation from Berkeley Earth Surface Temperature, this study reveals an unusually weak signal of summer SAT change in NC since the 1960s. This uniquely weak signal can be attributed to the small net contribution of external forcings, mainly from anthropogenic greenhouse gases (GHG) and aerosols (AA). Compared to other land regions in the same latitudinal band, the GHG‐induced warming was weaker and AA‐induced cooling was stronger in NC, resulting in the weakest SAT change signal and thus the lowest signal‐to‐noise ratio. This weaker GHG‐induced warming plays a more important role in the SAT change difference between NC and other land regions in the same latitudinal zone. Under different emission scenarios in the future, the signal‐to‐noise ratio in NC will become as large as those in the other land regions in the same latitudinal band and the northern hemisphere, which is partly due to the smaller relative differences in the SAT change signal between NC and the other two regions. The projections of SAT change indicate that climate change in NC will probably become more violent and vulnerable. [ABSTRACT FROM AUTHOR]

Exposure biases are a pervasive non‐climatic change in land air temperature records which have been introduced as a result of changes in the way thermometers were sheltered from solar radiation and the elements over time. Exposure biases have not been widely accounted for in observational records, due to difficulties detecting/correcting the bias using traditional homogenisation techniques; therefore, exposure biases still contribute significant uncertainty to the early period in global temperature compilations. Here, an empirical approach to address the bias arising from the introduction of Stevenson‐type screens from the late‐19th century is presented. The approach consists of: (1) an empirical analysis of 54 parallel measurement series to identify the characteristics of the exposure bias in four exposure classes; (2) the development of bias‐estimation models based on an analysis of which variables influence the bias; and (3) the application of the models to an extended version of CRUTEM5 (CRUTEM5_ext), based on exposure metadata, to quantify and reduce the bias. Step one identified differences between the temperatures recorded in Stevenson screens and early exposures, which vary seasonally, diurnally, and with location and exposure class. The largest biases (in mean temperatures) were found in freestanding exposures (up to −0.78°C annually) and in summer, while the smallest biases were generally found in wall‐mounted exposures (near‐0°C annually) and in winter. Significant relationships between the bias and temperature, downward top of atmosphere and/or received shortwave downward solar radiation were found in each exposure class and led to the development of three regression‐based bias‐estimation models. Application of these models to 1,960 mid‐latitude stations in CRUTEM5_ext, resulted in small (≤0.016°C) positive adjustments to the Northern Hemisphere mid‐latitude mean before 1880, and larger (≤−0.1°C) negative adjustments to the Northern and Southern Hemisphere mid‐latitude means between 1882–1934 and 1856–1900, respectively. Larger adjustments were estimated regionally: up to −0.57°C annually and −0.79°C seasonally in individual grid cells. [ABSTRACT FROM AUTHOR]

The climatology and variability of the January to February (JF) season in eastern Africa's (EA) precipitation are examined during the 1960–2020 period, as off‐season climate could have dire consequences, considering agricultural practices tie to the seasonal cycle of precipitation. The analysis in this study is divided into four parts. The first is the climatological background of variability during the JF season. Second, the spatiotemporal variability of the leading mode of the JF precipitation is described using an empirical orthogonal function (EOF) method. Third, anomalous atmospheric circulations linked to the variability of the JF precipitation were examined through composite analysis. Fourth, the link between JF precipitation and sea surface temperature (SST) is explored using composite and correlation analyses. The leading mode (EOF1) shows a monopole variation, with a positive anomaly in the entire region accounting for 55.1% of the total variance. EOF1 is linked to the SST anomaly (SSTA) over the tropical Indian Ocean (TIO). A warm (cool) SSTA in the TIO induces diabatic warming/adiabatic cooling (diabatic cooling/adiabatic warming). This leads to the rising (sinking) of warm and moist air (cold and dry air) from the lower to higher (higher to lower) troposphere via the ascending (descending) branch of the Walker circulation and contributes to the upper warm (cold) temperature anomaly centred at ~300 hPa. The warm (cold) anomaly is closely associated with the upper‐level westerly (easterly) and divergence (convergence) anomalies at the upper side of the warm (cold) core, coupled with ascending (descending) and deep wet (dry) anomalies below the warm (cold) core. This induces moisture convergence (divergence) and unstable (stable) conditions that favour (suppresses) precipitation over EA. Consequently, this study may facilitate the prediction of the JF precipitation and decrease in socio‐economic losses in EA. [ABSTRACT FROM AUTHOR]

Deforestation in the Brazilian Amazon (BA) for cattle and soybean production has significant consequences for the various aspects of the climate system. Land surface modifications due to deforestation directly influence surface energy and moisture availability, hence impacting rainfall patterns, air temperature and the onset of the agricultural rainy season. Here, we assess the forest loss‐related climate risks for the first and second crop seasons of the soy‐maize double cropping system in the BA. We utilized long‐term, daily, remote sensed climate data and annual land‐use maps as input for a machine learning algorithm to isolate the signal of forest loss on the climate. Our findings indicate that forest loss in the BA intensifies the risks of climate change from the local to the regional geographical scale, with the impact being more pronounced at the regional scale. Between 1999 and 2019, largely deforested regions exhibited a delay of approximately 76 days in the onset of the agricultural rainy season. These regions also experienced a 360 mm decrease in rainfall and an increase in maximum air temperature of 2.5°C. In view of these results, there are collective advantages of halting deforestation. Conservation of the Amazon Forest is vital for maintaining the early onset of the agricultural rainy season, favourable temperatures and adequate rainfall volume needed for attaining high yields in the soy‐maize double cropping system. [ABSTRACT FROM AUTHOR]

This article analyses the winter warm spells (WWS) that occurred in central Mediterranean over the period 1993–2022, examining the daily maximum temperatures collected at eight airport sites located in the Italian Peninsula, belonging to different climate zones. According to the definition proposed in 1999 by the Expert Team on Climate Change Detection and Indices (ETCCDI), a WWS is a sequence of at least six consecutive days when daily maximum air temperature exceeds the calendar day 90th percentile centred on a 5‐day window for a base period. WWS occurring over the entire Italian territory or only over northern/central/southern Italy have been identified and related to the peculiar synoptic conditions. It was found that December is the month most prone to WWS and, on average, WWS last 9.4 days in northern Italy, 6.6 days in central Italy, and 8.5 days in southern Italy. Over the period under investigation, the Italian Peninsula experienced only one common event characterized by persistent high‐pressure systems associated with air subsidence over western Mediterranean and, therefore, with exceptional warming. Finally, it has been proven that the definition of WWS proposed by ETCCDI allows to capture synoptic scale events but, in orographically complex areas such as Italy, underestimates moderate spells, which generally might have a duration of at least 3 days. Consequently, it is important to consider the possibility of reducing the period length threshold used for the detection of WWS when orographically heterogeneous regions are studied. [ABSTRACT FROM AUTHOR]

The relative importance of oceanic modes to global surface air temperature (SAT) and terrestrial precipitation during 1934–2020 was investigated using a variety of statistical and dynamical system methods. Through singular spectrum analysis, we present the distribution of decadal (10–20‐year), multidecadal (20–50‐year), and secular (>50‐year) variabilities of global SAT and terrestrial precipitation anomalies. Three sea surface temperature modes were identified by singular value decomposition that affect the low‐frequency variabilities of global SAT and terrestrial precipitation anomalies—namely, global warming (GW), the Interdecadal Pacific Oscillation (IPO), and the Atlantic Multidecadal Oscillation (AMO). The relative importance GW, the IPO, and the AMO in SAT and terrestrial precipitation was obtained through a normalized multivariate information flow analysis. Besides a high information flow percentage (60%) from GW to global SAT, especially over the northern Indian Ocean and tropical West Pacific, information flow from the AMO to the northwestern Pacific was also found. In terms of terrestrial precipitation, a large area with a wet trend was found over Eurasia at mid‐to‐high latitudes, and this trend was especially remarkable in the boreal winter half‐year (November–April), as compared with that in the boreal summer half‐year (May–October). By employing artificial neutral networks with a self‐organized map to cluster the key patterns of vertically integrated water vapour flux, we found that the synoptic circulation related to the wet trend is characterized by westerly flow that transports water vapour from the northeastern Atlantic to Eurasia, which is favourable for precipitation there both in the boreal winter and summer half‐year. [ABSTRACT FROM AUTHOR]

The diurnal temperature range (DTR) has generally decreased at global scale according to the IPCC reports and previous publications. Here, we examined the change in DTR within the past four decades and found that DTR decreased in wet areas, but it increased in dry areas. This is because the daily minimum air temperature (Tmin) increased at a slower rate than the daily maximum air temperature (Tmax) in the dry areas, while it increased more rapidly in the wet areas. The changes in cloud cover, water vapour and moisture flux were observed to have contributed to the observed change in DTR. The change in moisture flux largely contributed to this change through the indirect effect on the change in water vapour across the aridity gradient. The significant impact of moisture flux is linked to the differential change in moisture flux between the dry and wet zones. [ABSTRACT FROM AUTHOR]

Meteorological records at Signy Station in the South Orkney Islands (SOIs) have recently been digitized to cover the period of 1947–1995. This study compares the newly available near‐surface air temperatures at Signy with those from a nearby station, Orcadas and with reanalysis datasets to provide a more comprehensive picture of the weather and climate variability in the SOIs. Temperatures from both stations show a higher degree of variability in winter than summer, but the variability differs in terms of its relationship to the dominant wind directions and sea ice influences. The two stations differ markedly in terms of their respective warm temperature events, largely due to orography‐induced föhn winds at Signy Station as northwesterlies flow over Coronation Island. ERA5 reproduces the monthly to annual averages exceedingly well but underestimates both cold and warm tails of station temperatures. Temperature trends in the SOIs are also considered in terms of changes in large‐scale circulation and the sea surface temperature over the Brazil‐Falkland Confluence. However, caution is required in interpreting the long‐term temperature trends estimated from reanalysis data as most of the reanalyses show a cold bias before 1979, which is most likely caused by misrepresentation of the sea ice. [ABSTRACT FROM AUTHOR]

The emergency of global‐scale hydroclimatic extremes (i.e., meteorological droughts, extreme precipitations, heat waves and cold surges) and associated compound events has recently drawn much attention. A global‐scale unified and comprehensive event set with accurate information on spatiotemporal evolutions is necessary for better mechanism understanding and reliable predictions in sequential studies. Accordingly, this manuscript describes the first‐generation global event‐based database of hydroclimatic extremes produced with the newly proposed 3D (longitude–latitude–time) DBSCAN‐based workflow of event detection. The short name of this database is Glo3DHydroClimEventSet(v1.0), which is obtained from the FigsharePlus webpage (https://doi.org/10.25452/figshare.plus.23564517). The 1951–2022 ERA5‐based multiscale and multi‐threshold daily running datasets of precipitation and near‐surface air temperature are calculated and employed as the input data. A comprehensive event set of hydroclimate extremes is the output of the 3D DBSCAN‐based workflow. From perspectives of spatiotemporal evolutions, this event‐based database is also measured and attached with metric information. For case‐based validation, some recently reported hydroclimatic extremes (e.g., the 2020 summertime flood‐inducing Yangtze River extreme precipitation event) are employed and accurately detected in the Glo3DHydroClimEventSet(v1.0) database. Meanwhile, global‐scale spatiotemporal distributions are preliminarily analysed. For example, global‐scale event counts of extreme heatwaves displayed an increasing tendency since 2005, with a rapid increase after 2010. To sum up, this Glo3DHydroClimEventSet(v1.0) database may facilitate new scientific achievements concerning event‐based hydroclimatic extremes, especially in communities of atmosphere, hydrology, natural hazards and associated socioeconomics. [ABSTRACT FROM AUTHOR]

Indian subcontinent witnessed a rise in surface air temperature (SAT) in recent decades, during the summer months of March, April and May. The monsoon core region (MCR) of India experiences a hot and humid climate, with temperatures typically highest in May and June before the onset of the monsoon. Global climate model (GCM) simulations of SAT are very much essential to understand the future climate of Indian MCR. Biases in GCMs simulations are due to insufficient knowledge of parameterizations and various assumptions that are made to simulate the complex interactions between land, ocean and atmosphere. The objective of this study is to correct the bias in the Coupled Model Intercomparison Project Phase 6 (CMIP6)–GCM simulations of SAT during March, April and May months over MCR for the historical period 1985–2014 and shared socio‐economic pathways (SSPs) SSP2‐4.5 and SSP5‐8.5 for the period 2015–2100. SAT dataset of fifth‐generation reanalysis (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) is used as reference dataset to perform bias correction for the historical period. Preliminary investigation of both SAT datasets has shown that there exists considerable warm bias (1.47°C) over the MCR. Bias correction is performed using a one‐dimensional convolutional neural network (CNN‐1D) and a convolutional long short‐term memory network (CNN‐LSTM) deep learning algorithm. The performance of these algorithms is evaluated with the statistical metrics such as root‐mean‐square error (RMSE), normalized root‐mean‐square error, Nash–Sutcliffe efficiency, mean absolute error, percent bias, correlation coefficient and dynamic time warping. RMSE and percent bias were decreased to 0.35°C and 0.8% with CNN‐LSTM algorithm. The CNN‐LSTM algorithm also preserves the year‐to‐year variability of SAT. Hence, CNN‐LSTM algorithm is found to be suitable for the bias correction of GCM simulations of SAT with encouraging results. [ABSTRACT FROM AUTHOR]

Extreme thermal conditions have been known for a long time as is evidenced by the oldest preserved historical accounts referring to weather. However, direct data, that is, air temperature measurements, which began in Europe in the 17th century, are the only reliable source of information. They provide insight into how climate evolved over the characteristic period of the climate timeline known as the Little Ice Age. This study focuses on extreme climatic phenomena, referred to here as exceptionally cold (EC) and exceptionally warm (EW) months and seasons that occurred in the early instrumental period (until 1830). The research is based on average monthly air temperature values (of different lengths) from five meteorological stations in Europe. The Central England Temperature series (CET) offers the longest series that spans 172 years whereas Padua has the shortest, 57 years. Consequently, the year 1659, which is the start date of the Central England Temperature series, marks the beginning of the study period for the research presented in this article. Thermally anomalous periods are identified based on the standard deviation of the average temperature from the corresponding long‐term average (1831–2020). The highlights of the study include the identification of the pronounced prevalence at all five stations of EC months and seasons when compared to EW months and seasons, especially in Western Europe (CET) during the Maunder Minimum. The study also finds a lack of synchronicity in the timing of the anomalous periods under investigation in the different parts of Europe. [ABSTRACT FROM AUTHOR]

The oldest meteorological station in the Polish Tatra Mountains is that on the Gąsienicowa Glade (1520 m). In this study, two series of observations for the years 1927–1938 and 1947–2020 are presented for the number of days with snow cover higher than 1 cm (SCD—from 142 to 228 days), maximum snow depth (HSmax—from 43 to 271 cm) and average snow depth (HSmean—from 10 to 93 cm). For the remaining meteorological parameters discussed in this article, the observation series is slightly shorter. From 1927 to 2020, the number of days with snow cover show downward trends (SCD—from nearly 200 days in 1927/1928 to about 175 days in 2019/2020). The decrease in the values of SCD discussed is due to an increase in air temperature and an upward trend in the percentage of the number days with rain over the number of days with snowfall. The increase in mean air temperature started in the 1980s. The strongest temperature trends were recorded in spring (TG in June—from 8.2°C in 1927 to 10.0°C in 2020) and in summer (TG in August—from 9.7°C in 1927 to 12.0°C in 2020). The last two decades, in particular, are exceptional for the Tatra Mountains, both in terms of air temperature, days with snow cover, maximum and mean snow depth. Since the beginning of observations in the Gąsienicowa Glade, no such strong decade‐long increases in temperature and decreases in the number of days with snow cover and in maximum and mean snow depth have been recorded. The observed trends in days with snow cover and maximum and mean snow depth in the Tatras are similar to those observed in studies conducted in the Alps. [ABSTRACT FROM AUTHOR]

This study analyzes the long‐term observed changes of mean (Tmean), maximum (Tmax) and minimum (Tmin) air temperatures, relative humidity (RH) and vapour pressure deficit (VPD) at different elevation ranges across Bolivia from 1950 to 2019. The linear trends in air temperature series present a significant increase, with no substantial seasonal or spatial differences. On an annual basis, RH exhibited a non‐significant decrease (−0.08% decade−1), while VPD showed a significant increase (0.01 hPa decade−1) (p < 0.05). Although prior research has suggested that highland elevations experience faster warming than the global average, we have not identified a distinct correlation between elevation gradients and differential warming rates in Bolivia. Future research could investigate elevation‐dependent climate trends by examining seasonal and monthly patterns of climatic variables in relation to topographical gradients in various highland regions. [ABSTRACT FROM AUTHOR]

This study evaluates the reliability of ERA5 and ERA5‐Land reanalysis datasets in describing the mean daily air temperature of four climate domains in mainland Portugal. The reanalysis datasets were compared with ground observations from 94 meteorological stations (1980–2021). Overall, the results demonstrated a good degree of correlation between the observed and reanalysis data on both a daily and seasonal scale. Both the latitudinal distribution of the air temperature and the moderating effect of the Atlantic Ocean are well described. However, in the case of Portugal, the ERA5‐Land was shown to be considerably more effective at describing the mean daily air temperature than ERA5. The results also indicated that, in general, the reanalysis methodologies perform better when applied to air temperature simulation in flatter regions as opposed to regions with high‐altitude and complex terrain. The study further suggests that ERA5 and ERA5‐Land reanalysis should be used with caution in the case of short‐term environmental studies. In fact, relevant differences were shown to exist between the reanalyses and the observed daily mean air temperature datasets for certain specific years. Overall, considering the RMSE between the ERA5‐Land reanalysis datasets for mean daily air temperature and the observed datasets there is a 28% probability of locally having a mean RMSE <1.5°C, 52% probability of having a mean RMSE >1.5°C and <2.0°C, and 16% probability of having a RMSE >2.0°C and <3.0°C. These conclusions will hopefully contribute to improving our understanding of the uncertainty sources in relation to ERA5 and ERA5‐Land reanalysis data for different climate domains. [ABSTRACT FROM AUTHOR]

Evapotranspiration is an important part of the water cycle and influences, for example, water management and agricultural production. The article presents temporal and spatial changes in reference evapotranspiration (ETo) in the growing season (April–September) against the background of climatic conditions in Poland in the years 1966–2020. ETo was calculated using the Penman–Monteith method using data from 37 meteorological stations in the network of the Institute of Meteorology and Water Management‐National Research Institute. For the growing season of April–September, the average sum of ETo for Poland was 529 mm. In terms of spatial distribution, the values were lowest in northern Poland (Kołobrzeg, 480 mm; Lębork, 488 mm) and highest in the central lowlands (Warsaw, 570 mm; Poznań, 566 mm). For individual months, the average sum of ETo ranged from 53 to 63 mm in April and September to 108–112 mm in June and July. In Poland, in the years 1966–2020, ETo showed a statistically significant upward trend of 16.4 mm/10 year. This is the result of, inter alia, statistically significant increases in sunshine duration and air temperature and a decrease in relative humidity (respectively: 62 h/10 year, 0.40°C/10 year and −1%/10 year) during the growing season in Poland. Within the multiyear study period, the maximum ETo values occurred in 2018, reaching values of >700 mm. At that time, Poland was under the influence of very intense anticyclonic circulation. This is confirmed by the relationship between ETo and the anticyclonic circulation index for central Poland, which is statistically significant for the 55 study years. [ABSTRACT FROM AUTHOR]

Based on the ERA5 reanalysis since the 1950s, this study investigates the coherent inter‐decadal variations of the winter continental surface air temperature (SAT) between the Eurasian (EUA) and the North American continents in the past 70 years. The SAT variation over the two continents can be characterized with five typical periods successively from a 'both‐warmer' (1950s–1960s), to a 'both‐colder' (1960s–1980s), and then a 'EUA‐warmer' (1980s–1990s) followed by another 'both‐warmer' (1990s–2000s) and a 'EUA‐colder' (2000s–2010s) period. Such periodic variation of the SAT is closely coupled with changes of the low isentropic‐level meridional mass transport (IMMT), as manifested by the coupling relationship between the midlatitude SAT and the net IMMT out of the polar circle, in terms of their zonal patterns and geographical patterns. The SAT variation can be well reconstructed with the first two leading maximum covariance analysis (MCA) modes between the SAT and IMMT. The first leading MCA mode (MCA1) mainly dominates a 'EUA‐warmer/colder' pattern, while the second (MCA2) dominates the 'both‐warmer/colder' pattern. There exists a significant lead–lag correlation between the MCA1 and MCA2, which determines the changing SAT pattern in the five successive periods. Furthermore, the MCA1 and MCA2, especially their lead–lag coupling after the 1975 is significantly associated with the Pacific decadal oscillation (PDO) which dominates the much‐intensified MCA1 and the related 'EUA‐warmer/colder' SAT pattern since then. The Atlantic multi‐decadal oscillation mainly dominates the third leading MCA mode with it's effects on the SAT over Europe coincidently to amplify the PDO‐related SAT changes. [ABSTRACT FROM AUTHOR]

Six gridded temperature datasets (T‐datasets) were evaluated for the first time over the South American continent, through the case study of Bolivia, by comparing them with temperature records acquired from 82 meteorological stations spanning the 1995–2010 period. The comparisons were carried out at the daily time step considering different seasons (annual scale, austral summer and austral winter) and regions (Amazon, La Plata and Altiplano basins). Overall, the climate hazards group infrared temperature with stations (CHIRTS) and the climate prediction centre (CPC) T‐datasets provided the most reliable mean daily temperature (Tmean) and also described well the temporal variability of minimum and maximum daily temperature estimates (Tn and Tx). Tmean, Tn and Tx trends were analysed over the 1983–2016 period to observe temperature temporal evolution across the three regions. Despite some general agreements between the trends (Tmean, Tx and Tn), large discrepancies are also observed. It was found that CPC overestimates and CHIRTS underestimates mean temperature trends and that CPC (CHIRTS) was better than CHIRTS (CPC) to estimate Tx (Tn) trends, both in magnitude and space. Furthermore, opposing trends (i.e., warming and cooling) are described by CPC and CHIRTS for some specific regions, which call into question their reliability for such analyses. These findings highlight the need to validate gridded temperature products with reliable ground data for the regions under study, particularly if they have a wide elevation range. [ABSTRACT FROM AUTHOR]

The air temperature anomaly was analysed in 16 consecutive winters from 2004 to 2019 in southern Brazil using reanalysis data and hourly atmospheric observations. In this study, 13 heat wave (HW) episodes were identified using the percentile methodology. All identified episodes are associated with the occurrence of a downslope windstorm known as Vento Norte (VNOR; Portuguese for 'North Wind'). The VNOR is characterized by a sharp increase in air temperature combined with constant, strong, gusty winds from the north and a drop in relative humidity. The average duration was about 5 days for HW and 18 h for VNOR being than in 59.5% of the HW hours, the VNOR windstorm was also detected. The main atmospheric variables were compared during the periods of winter, HW and the VNOR windstorm. The analysis showed that HW alters local weather and climate conditions, with the more pronounced features associated with the VNOR flow. A more detailed investigation comparing the atmospheric variables collected north and south of an abrupt change in terrain altitude (400 m) revealed that the main cause of the amplification of the atmospheric variable anomalies in the centre of the state of Rio Grande do Sul was the simultaneous occurrence of HW and VNOR. [ABSTRACT FROM AUTHOR]

The air temperature difference is an essential indicator to measure weather and climate change. The Tibetan Plateau (TP) is a sensitive area to global climate change. The study on its temperature difference variations is of great significance for further understanding the evolution of weather and climate systems, and scientific disaster prevention and mitigation. In this study, the spatio‐temporal evolution characteristics of the temperature difference on daily, monthly, seasonal and annual scales and their possible causes are analysed based on the daily observations at 59 meteorological stations over the TP from 1961 to 2020 and the circulation indexes such as the East Asian summer monsoon, South Asian summer monsoon, midlatitude westerly and TP summer monsoon. The results indicate that the diurnal temperature range (DTR) on the TP tends to decrease from 1961 to 2020. Compared with the situation in the late 20th century, the annual average of the DTR in the eastern TP turns from a nonsignificant increasing trend into a pronounced decreasing trend, while that in the western TP maintains a decreasing trend. This phenomenon is primarily influenced by the minimum and maximum temperature, precipitation and atmospheric circulations. The standardized temperature difference at different temporal scales shows decreasing trends and is distributed in north–south and east–west distribution patterns. The annual temperature range is mainly influenced by the remarkable increase of the minimum temperature, especially in the northern and southeastern TP. Besides, the increases of maximum temperature and precipitation also affect the annual temperature range. In terms of the seasonal temperature range, it is dramatically influenced by different meteorological factors, especially in the northern TP. The monthly temperature range (Mon‐TR) is dramatically influenced by the maximum and minimum temperature in the first and second half years, respectively. Moreover, the circulation systems, such as the midlatitude westerly, South Asian summer monsoon and TP summer monsoon, are the main influencing factors of the temperature difference variations on the TP. The midlatitude westerly have a large influence on Mon‐TR in June and September, and the significantly influenced stations reaches 54.2% and 35.6%. The impact stations of the TP summer monsoon on Mon‐TR is the largest in August, and the significantly influenced stations is 39%. In July, the influenced stations of the TP summer monsoon and South Asian summer monsoon on the Mon‐TR reaches 30.5%–33.9%. The influenced area of the Mon‐TR in June is largest affected by single summer monsoon or westerly index. Previous studies have shown that the warming mechanisms and their influencing factors have changed in the TP since the 21st century. The study results might provide the foundation for a scientific understanding of climate warming on the TP. [ABSTRACT FROM AUTHOR]

The CMIP6 projections constitute the basis of our latest understanding of the climate response to anthropogenic forcing. However, there is still considerable uncertainty in the projections, especially at the regional scale. One way to constrain the uncertainty is by comparing the models historical climate change signals against observations and investigate the physical reasons for divergences. Here, we assess the signal‐to‐noise ratio (S/N) of surface air temperature (SAT), precipitation (PREC) and soil moisture (SM) over Europe for a set of CMIP6 historical simulations and compare them against the E‐OBS observational product and the ERA5 reanalysis. We found considerable divergences between the CMIP6 ensemble mean S/N and that of E‐OBS and ERA5, as well as between ERA5 and E‐OBS. The latter indicates that the S/N is affected by data coverage. We show that the differences among model signals are associated with different atmospheric circulation responses. We also investigate the potential relationships between the models' signals and climatological biases, and we found evidence that the models with a warm climatological bias in southern Europe tend to have smaller SAT signals (warm less). Finally, we found no apparent relationship between SM biases and the warming signal, suggesting that the mechanism by which SM–atmosphere interactions affect climate variability does not explain the mean changes. However, there is a tendency for models with higher SM to dry faster than models with lower SM. [ABSTRACT FROM AUTHOR]

Several atmospheric variables are directly influenced by oceanic conditions, mainly the sea surface temperature (SST). SST trends and variability over the southwestern Atlantic (SWA) have affected the South American climate, principally the monsoonal period (austral summer). This study analysed the SST trends over the SWA for all seasons. In addition, possible large‐scale triggers of the austral summer's warmest SST over the SWA and the straight impacts of these warmest SSTs on the South American climate were investigated. The results showed a significant positive SST trend of approximately 0.02°C·year−1 over the SWA for all seasons. Since 2000, positive SST anomalies have increased in frequency in all seasons, with the seven warmest summers occurring during the 2010s. The highest SST summers over the SWA have been related to four leading causes: (i) planetary waves triggered by warm anomalies over the subtropical South Pacific; (ii) an omega‐type blocking configuration over the South Atlantic; (iii) a Southern Annular Mode positive phase; and (iv) a strengthening of the Hadley Cell, responding to warm SST over tropical North Atlantic. They all reflected on intensification of the South Atlantic Subtropical High and the western boundary current, heating the SWA. The summer's warmest SST led to positive air temperature anomalies extending in almost all of eastern South America, with significantly highest temperatures over southeastern Brazil, northern Argentina and western Paraguay. Clouds decrease strengthen the incident shortwave in southeastern Brazil, warming the region. Significant reduction of cloudiness and precipitation indicate a low performance of the South Atlantic Convergence Zone. The comparison of the warmest summers in 1980s–1990s in the SWA ratified the role of SST intensification. In conclusion, the positive SST trends over the SWA are related to the summer's warmest SST after 2000s, causing heating and dryness in eastern South America, mainly over southeastern Brazil. [ABSTRACT FROM AUTHOR]

This article discusses the reasons for shortening snow cover duration in the Western Sudetes, considering local changes in: air temperature; amount and type of precipitation; sunshine duration and atmospheric circulation leading to changes in the number of days with snow cover and its depth; and its start and end dates. All factors were linked to the exposure and relief of the study area. The analysis was made for the winter seasons (Nov–Apr) 1961/1962–2020/2021. It was found that the primary reasons for the shortening of snow cover duration in the Western Sudetes are: a multi‐year increase in air temperature and sunshine duration; changes in precipitation patterns—a decrease in the proportion of solid precipitation, changes in atmospheric circulation—including an increase in anticyclonic circulation types with sunny weather, especially in April (snow cover disappears in most of the elevation profile of the Sudetes); and less cyclonic weather types. The above factors synergistically affect the lower snow depth, and fewer days with solid precipitation, which promotes its faster spring ablation. In the subsequent 30 years (climatological norms), there is a successive shortening in its duration. On the snow cover start dates, there are no clear trends in the direction and rate of change. On end dates, negative trends are observed, in most cases statistically significant. The rate of change for the end dates of snow cover is about twice as high as the start dates. The rate of decline in snow cover is higher at stations at similar altitudes with northern macro‐exposure than southern. The results correspond with other studies from Europe and the world on the earlier disappearance of snow cover. They confirm the successive global warming and shortening snow cover duration, especially evident in the last few decades. [ABSTRACT FROM AUTHOR]

Based on the daily average temperature data of 2374 stations in China from 1993 to 2022 and the monthly average temperature data of European Centre for Medium‐Range Weather Forecasts (ECMWF) System5 in winter, this article analyses the intraseasonal variations of winter surface air temperature (SAT) over China by using seasonal empirical orthogonal function decomposition (S‐EOF) and compare the prediction skill differences of the first three dominant modes in ECMWF System5. The first mode (S‐EOF1) is characterized by out‐of‐phase changes in SAT anomalies (SATA) between December and January. Both the Siberian high (SH) and the central Pacific El Niño‐Southern Oscillation (CP ENSO) can impact S‐EOF1. The second mode (S‐EOF2) is characterized by consistency in warm anomalies over the Northeast and Qinghai‐Tibet Plateau's variation, while the other areas are characterized by the out‐of‐phase change in SATA. S‐EOF2 is closely related to Arctic oscillation (AO). The third mode is characterized by alternating changes over 3 months in winter, which is related to the Pacific meridional mode (PMM) and the North Atlantic SST tripole (NAT). Comparing the ECMWF System5 prediction skill for the first three leading modes for the intraseasonal variations in winter SAT, the prediction skill is not high. The model did not predict the extent of warm anomalies and the intraseasonal fluctuation in S‐EOF1, which may be due to the poor prediction skills for the range and intensity of the CP ENSO and the SH. The second mode has the highest prediction skill among the three modes. For the prediction skill of AO is good. The model can capture the intraseasonal reverse SATA between December and January but did not capture the reverse SATAs between January and February. Because the model does not predict obvious PMM and NAT well. [ABSTRACT FROM AUTHOR]

In this study, multi‐year near‐surface observations were conducted at two sites in Jakarta, Indonesia to analyse seasonal sea breeze variation. Seasonal length was defined using observed zonal wind components, the rainy season was defined as December–March, and the dry season was defined as May–September. We found that the sea breeze in Jakarta started earlier, propagated more rapidly, and lasted for a shorter period of time during the rainy than dry season. Variation in the air temperature difference between urban and coastal areas of Jakarta was the major factor driving sea breeze seasonal variation. During the rainy season, night‐time cloud downwelling decreased this air temperature difference, causing earlier sea breeze onset and more rapid sea breeze propagation due to a weaker land breeze. By contrast, during the dry season, intense night‐time radiative cooling inland caused a strong negative temperature difference that produced a stronger land breeze, thus, slowing sea breeze propagation. Seasonal differences in urban surface heating and urban heat island circulation may also affect sea breeze onset and propagation speed. Discrepancies in thermal properties between urban core and coastal areas of Jakarta also prolonged positive temperature differences after sunset, thus extending the sea breeze duration in the dry season. [ABSTRACT FROM AUTHOR]

The frequency and magnitude of global warming events varies greatly across different regions and countries. The climatic diversity for China and future warming features are projected across 12 climatic zones based on the ensemble of the five well‐performing high‐resolution downscaled climate models for each zone. There are warming patterns for the mean near‐surface air temperature (Tm), maximum near‐surface air temperature (Tmax), minimum near‐surface air temperature (Tmin) as well as heatwave and frost events. Under RCP4.5 and RCP8.5 scenarios, the three indices (i.e., Tm, Tmax and Tmin) countrywide are likely to increase at respective rates of 0.30–0.31 and 0.64–0.67°C·decade−1. The extent of frost event (FE) are projected to decrease at a rate of −1912 and −4442 day·km−2·decade−1 while the extent of heatwave event (HE) increase at a rate of 1116 and 3557 day·km−2·decade−1 under RCP4.5 and RCP8.5 scenarios, respectively. A higher increment in temperatures, a decreasing trend in diurnal temperature range (DTR), frost days and FE are highlighted in the western and northern zones, while these trends are opposite in southern zones. The warming is likely to get faster in the current colder zones (western and northern zones) while heatwave is more intense and severe in Jianghuai, Jianghan, the south Yangzi River, South China and Xinjiang. These potential changes indicate that adaption and mitigation strategies are necessary in response to future warming. [ABSTRACT FROM AUTHOR]

Mozambique is highly dependent on subsistence agriculture. Thus a climatology of rainfall onset and cessation is derived together with their interannual variability and trends using gridded daily data and station data. Mean onset is found to occur from early to mid‐November over southernmost regions, mid‐ to late November over central regions and late November to early December over the northern and coastal regions while cessation is from mid to late March over southern and late March to early April over most of central and northern Mozambique. The two gridded datasets showed differences (~30 days) in rainfall onset and cessation dates over southern Mozambique with standard deviations of 20–35 days. Timing of early onset over northern and central Mozambique appears associated with both zonal and meridional moisture convergence and tropical extratropical cloud bands, while timing of late onset is associated with southeast moisture fluxes from the subtropical South Indian Ocean and uplift over the landmass. Early (late) onset appears to be linked to La Niña (El Niño), particularly over central Mozambique. Rainfall cessation over most of Mozambique tends to be early (late) during El Niño (La Niña) summers. The relationship of ENSO with onset and cessation over large areas in Mozambique may help farmers and other user groups in planning their activities for the rainy season. [ABSTRACT FROM AUTHOR]

Precipitation is a crucial influencing factor in the global hydrological cycle and terrestrial ecosystems. Previous studies have mostly focused on the historical precipitation variations over the Tibetan Plateau (TP). However, little attention has been given to the differences between convective and stratiform precipitation under future climate change scenarios. In order to acquire the high‐resolution precipitation information over the TP, the dynamical downscaling simulation was conducted utilizing a regional climate model (Weather Research and Forecasting [WRF] model) forced with a global climate model (Community Climate System Model [CCSM]). In this study, we focus on projections of future changes in convective and stratiform precipitation over the TP. The downscaling results during a 26‐year period 1980–2005 are compared for dry season, wet season and annual mean precipitation against the gridded observation dataset. Then, the changes in the wet season convective and stratiform precipitation with warming between CCSM and WRF‐CCSM were compared in the future period 2070–2099 under two scenarios (RCP4.5 and RCP8.5). Compared with the coarse‐resolution forcing, the historical precipitation of dry season, wet season and annual mean over the TP were found to better reproduce in WRF‐CCSM simulation. WRF‐CCSM projects an opposite spatial pattern of total precipitation change between the northern and southern TP in the future under different scenarios, which were attributed to the decreased stratiform precipitation in the southern TP and the increased convective precipitation in the northern TP, contrasting with the uniform increase of total precipitation in its forcing. The surface air temperature projected in WRF‐CCSM is larger increase than in the CCSM forcing. Compared to the CCSM forcing, WRF‐CCSM projects more pronounced increase in convective precipitation in response to rapid warming, and increasingly dominates changes of total precipitation with temperature exceeds the Clausius–Clapeyron rate over the northern TP. [ABSTRACT FROM AUTHOR]

Precipitation is a crucial influencing factor in the global hydrological cycle and terrestrial ecosystems. Previous studies have mostly focused on the historical precipitation variations over the Tibetan Plateau (TP). However, little attention has been given to the differences between convective and stratiform precipitation under future climate change scenarios. In order to acquire the high‐resolution precipitation information over the TP, the dynamical downscaling simulation was conducted utilizing a regional climate model (Weather Research and Forecasting [WRF] model) forced with a global climate model (Community Climate System Model [CCSM]). In this study, we focus on projections of future changes in convective and stratiform precipitation over the TP. The downscaling results during a 26‐year period 1980–2005 are compared for dry season, wet season and annual mean precipitation against the gridded observation dataset. Then, the changes in the wet season convective and stratiform precipitation with warming between CCSM and WRF‐CCSM were compared in the future period 2070–2099 under two scenarios (RCP4.5 and RCP8.5). Compared with the coarse‐resolution forcing, the historical precipitation of dry season, wet season and annual mean over the TP were found to better reproduce in WRF‐CCSM simulation. WRF‐CCSM projects an opposite spatial pattern of total precipitation change between the northern and southern TP in the future under different scenarios, which were attributed to the decreased stratiform precipitation in the southern TP and the increased convective precipitation in the northern TP, contrasting with the uniform increase of total precipitation in its forcing. The surface air temperature projected in WRF‐CCSM is larger increase than in the CCSM forcing. Compared to the CCSM forcing, WRF‐CCSM projects more pronounced increase in convective precipitation in response to rapid warming, and increasingly dominates changes of total precipitation with temperature exceeds the Clausius–Clapeyron rate over the northern TP. [ABSTRACT FROM AUTHOR]

The set of meteorological data from the Jagiellonian University station in Kraków is one of the longest‐recorded in central Europe. Measurement records date back to 1826. Based on the average monthly values of air temperature, total cloudiness, water vapour pressure and wind speed at 1200 UTC, the UTCI values were calculated and the occurrence of cold stress (UTCI < −13°C) and heat stress (UTCI > 32°C) was estimated. The variability of the applied measures of thermal stress conditions was studied both annually and in individual seasons (MAM, JJA, SON, DJF). Statistically significant changes in the studied characteristics of the bioclimate were found. Average annual UTCI values increased at the rate of 0.27°C/10 years. In individual seasons, the trend of UTCI changes ranged from 0.10°C/10 years in summer to 0.43°C/10 years in winter. The number of days with cold stress decreased by 1.8 days/10 years and the number of days with heat stress significantly increased (0.5 days/10 years). Two subperiods with different trends of changes were determined. The first covers the years 1826–1940, and the second, 1941–2021. The former indicates a negative UTCI trend of −0.17°C/10 years, and the latter, an increasing trend of 1.09°C/10 years. The last eight decades have brought a decrease in cold days of 6.5/10 years and an increase in heat days of 2.6/10 years. [ABSTRACT FROM AUTHOR]

A wet‐to‐dry shift in summer over the Sichuan Basin (SCB) during the late 20th century was noticed by previous studies. However, this study found that summer precipitation over SCB significantly increases during the past two decades, with an abrupt change point around 2011/2012. An anomalous cyclone over Lake Baikal, accompanied by an anticyclone over southern China, is responsible for the increase in SCB precipitation by inducing anomalous upper‐level divergence, mid‐level ascending motion and moisture convergence. Such atmospheric circulation anomalies are closely associated with the sea surface temperature (SST) warming over the Barents–Kara Sea (BKS). Observational analyses and model experiments indicate that the SST warming over the BKS warms local atmosphere and weakens meridional air temperature gradients over northern Asia and induces local anomalous easterlies, which further induce the anomalous cyclone over Lake Baikal that favours SCB precipitation. Additionally, the inhomogeneous warming over Europe increases local meridional air temperature gradients, enhances atmospheric baroclinicity and intensifies local transient eddy activities over central Europe. The intensified transient eddy activities generate negative geopotential height tendency anomalies over northern Europe, which can further excite a Rossby wave train that directly influences SCB precipitation, and can warm the atmosphere above the Kara Sea that indirectly influences SCB precipitation. This study can deepen our understanding of the interdecadal variability of summer precipitation over the SCB. [ABSTRACT FROM AUTHOR]

In this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data. [ABSTRACT FROM AUTHOR]

In 2021/2022 winter, an intraseasonal surface air temperature (SAT) reversal occurred in China, shifting from regional warm to cold phase in late January 2022. The intraseasonal variations of winter SAT in China are tightly connected with 500 hPa potential height. In particular, the Arctic Oscillation (AO) positive phase is diagnosed to be contributing to the SAT reversal. Under its influence, the intraseasonal build‐up and collapse of blockings over Urals Mountain led to the cold surges in February 2022. Seasonal prediction systems (SPSs) such as NCEP_CFSv2, EC_SYSTEM5 and BCC_CSM1.1 are assessed to have poor predictability in this SAT reversal and similar historical events, owing to not only the poor skills in winter SAT predictions but also the integral inertia in SPSs. Prediction skill only increases as the forecasting time approaches, owing to initial values. Besides, the SPSs can simulate the physical process of the influence on SAT from circulation. The poor predictability is attributed to the poor prediction skills in mid–high latitude circulation pattern and potential height over China. [ABSTRACT FROM AUTHOR]

The homogenization of climate observational series is a needed process before undertaking confidently any study of their internal variability, since changes in the observation methods or in the surroundings of the observatories, for instance, can introduce biases in the data of the same order of magnitude than the underlying climate variations and trends. Many methods have been proposed in the past to remove the unwanted perturbations from the climatic series, and some of them have been implemented in software packages freely available on the Internet. The Spanish project MULTITEST was intended to test their performance in an automatic way with synthetic monthly series of air temperature and atmospheric precipitation, to update inter‐comparison results from former projects, especially those of the COST Action ES0601. Several networks representing different climates and station densities were used to test a variety of homogenization packages on hundreds of random samples. Results were evaluated mainly in the form of root mean squared errors and errors in the trend of the series, showing that ACMANT, followed by Climatol, minimized these errors. However, other packages performed also relatively well, even outperforming them when there were simultaneous biases of the same sign in most or all the test series. [ABSTRACT FROM AUTHOR]

The fragile ecological environment of the Mongolian Plateau (MP) is sensitive to climate change. It is necessary to fully understand the temperature and precipitation changes on the MP to ensure regional sustainable development. Most existing studies are conducted based on station observations which suffer from sparse distribution and limited spatial representativeness and cannot perfectly represent the climatic conditions of the whole region. By contrast, the long‐term, spatially continuous reanalysis products offer new opportunities for MP climate change research. However, the coarse spatial resolution and large uncertainties of reanalysis data limit their applicability to provide reliable climate information at finer scales. Within the delta downscaling framework, we downscale and correct the state‐of‐the‐art European Centre for Medium‐range Weather Forecasts ReAnalysis 5 land portion (ERA5‐Land) using a high‐resolution WorldClim reference climatology and generate a long‐term (1950–2020) 1‐km monthly air temperature and precipitation downscaled dataset. During the downscaling process, the results obtained by nearest neighbour, bilinear and bicubic interpolation methods are evaluated and compared. Air temperature result of bilinear method and precipitation result of nearest neighbour method, which have the best accuracy, are taken as the final downscaled data. The evaluation shows that the downscaled data has acceptable accuracy (overall MBE = −0.41°C, Corr = 0.997, RMSE = 1.51°C for air temperature, and MBE = 1.58 mm·month−1, Corr = 0.747, RMSE = 24.24 mm·month−1 for precipitation), and outperforms the original ERA5‐Land and widely used Climatic Research Unit (CRU) data. In addition, the downscaled data can also provide accurate and detailed temperature and precipitation change trends over the MP. With fine spatial resolution, long‐time span and good spatial continuity, the downscaled air temperature and precipitation will be a useful data source to explore climate change over the MP. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) contains the largest permafrost region in the mid–low latitudes and the largest area of glaciers outside of the polar regions. In recent decades, this region has experienced vegetation greening (e.g., increasing leaf area index) due to climate change. As the largest exorheic river on the TP, the Upper Brahmaputra Basin (UBB) is very sensitive to climate change, experiencing the humidifying and significant warming. In this study, we investigated the spatiotemporal variability of frozen ground and vegetation over the last four decades in the UBB and explored how these changes have impacted river runoff using a water‐ and energy‐budget distributed hydrological model (WEB‐DHM). We found that almost 50% of permafrost transformed into seasonally frozen or unfrozen ground from 1981 to 2019 with the great improvement of vegetation leaf area index (LAI). Based on the variable‐controlling approach (set the air temperature or vegetation unchanged), we revealed that frozen ground degradation caused an average of 9.3 billion m3 of water loss per year, accounting for 5.4% of total UBB river runoff, even if frozen ground degradation can increase water resources at the early stage. However, vegetation greening has caused a runoff decline by 10.9 billion m3 (6.4%) annually due to enhanced evapotranspiration. These findings highlight that it is critical to understand and mitigate the impacts of changing frozen ground and vegetation, when managing water resources availability and ecosystem conservation under rapid climate change. [ABSTRACT FROM AUTHOR]

The present research focuses on temperature change signals over the Carpathian Basin with a special focus on selected lowland and mountainous subregions. High‐resolution (0.11°) EURO‐ and Med‐CORDEX regional climate model (RCM) simulations of near‐surface air temperature are analysed based on raw and bias‐adjusted data. The mini‐ensemble consists of eight RCM simulations driven by five different general circulation models for the period 1976–2099 under the high‐end RCP8.5 scenario. The high‐resolution, homogenized and quality controlled CARPATCLIM was used as a reference dataset. The selected subregions cover eight municipalities located at diverse altitudes: Bratislava, Budapest, Brassov, Debrecen, Hoverla, Novi Sad, Pécs and Poprad. The following climate indices are assessed: summer days, ice days, frost days, tropical nights, the coldest day, the warmest day, the coldest night and the warmest night. In general, for the reference period (1976–2005) bias‐adjusted RCM data showed almost perfect match with observations. Accordingly, no best performing RCM is found for all indices. The ensemble mean of the bias‐adjusted RCM simulations projects an increase (decrease) of 32% and 112% (18% and 25%) in the annual number of summer days and tropical nights (frost days and ice days) for the period 2021–2050. For 2070–2099 we can expect more frequent tropical nights (about five times) with respect to the reference period and the frequency of frost days can be halved. Profound warming manifests in the increase of the warmest temperature of day of up to 2–3°C by the near future and of 5–7°C by the end of the 21st century, which means the absolute maximum temperature can reach 44–47°C for the period 2070–2099. Our results also highlight the need for bias‐adjusted data adapted by different sectors (human health, agriculture, transport, disaster management, heritage conservation) under the national adaptation strategies. [ABSTRACT FROM AUTHOR]

The aim of the study is to characterize the long‐term variability of the low‐level cloud base heights (CBH) in Poland in the years 1971–2020. Data were used from six weather stations belonging to the Institute of Meteorology and Water Management – the National Research Institute (IMWM‐NRI), where CBH was first measured using the IWO device and, since the 1990s, using a ceilometer. The analysis allowed for data from eight observation periods (0000, 0300, 0600, 0900, 1200, 1500, 1800, 2100 UTC), which referred to several height ranges (between the ground surface and 2500 m a.s.l.). In the analysed multiyear period, in the cool half of the year, the height of the Cumulonimbus cloud bases increased significantly, that is, the number of cases increased at a height of >1000 m and, in the warm half of the year, at >1500 m. In recent years, the number of cases of the Cumulus cloud with a base at a height between 300 and 999 m has clearly decreased, while it has increased at a height of 1000–1499 m (in October–March) and 1500–1999 m (April–September). A similar increase in CBH was found in the case of the Stratocumulus cloud (their greater share between 1500 and 2499 m). It was only in the case of the Stratus cloud that a lowering of the cloud base was noticed, possibly owing to the imperfect measurement of the fractus species. The positive upward trend in the base height of most low‐level clouds can be explained by a statistically significant increase in temperature and a decrease in relative air humidity in the lower troposphere. [ABSTRACT FROM AUTHOR]

High‐resolution (dx = 25 km) simulations of the regional climate model RegCM4.7 coupled with the Community Land Model version 4.5 (CLM4.5) under low (RCP2.6) and high (RCP8.5) emissions scenarios, which interact with the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (AR5‐IPCC), were carried out over tropical South America (TSA). These simulations were produced through boundary conditions from simulations driven by the general circulation model HadGEM2‐ES. With the goal of verifying the added value (AV) of RegCM4.7, reproducing in an adequate and coherent way the regional aspects of the historical period (1986–2005), as well as evaluating the regional aspects simulated by the model in the scope of the change projections for the far‐future (2080–2099). For this study, the climate in the TSA was characterized based on the variables of precipitation and near‐surface air temperature. For the evaluation of the spatial–temporal representation, frequency and distribution of the regional and global simulation, the high‐resolution observational dataset of the Climate Research Unit version ts4.02 (CRU) was used. Although some differences and biases still persist, RegCM4.7 presents AV in the spatial representation of precipitation and temperature over the northeast region of Brazil and part of the Andes, especially in winter. However, it does not adequately represent precipitation over the Amazon Basin, especially in summer. Results for future projections indicate that the more refined simulation of RegCM4.7 improves the projected change patterns of the coarser resolution simulation of HadGEM2‐ES and even modifies the precipitation signal in some cases. Both models project increase temperature with greater magnitude for RCP8.5. RegCM4.7 presents a much more refined and realistic spatial distribution. HadGEM2‐ES simulates the major aspects of climate enough to consider forcing RegCM4.7 to generate simulations with better performance and more realistic projections. [ABSTRACT FROM AUTHOR]

This study examines the European heatwaves' predictability at subseasonal timescales. Land surface feedbacks and tropical convection, due to their variability at intraseasonal timescales, are taken into consideration and their potential role in extending the predictability beyond the medium range (10 days) is explored. A classification of European heatwaves into five heatwave types is used to discriminate the effects of surface feedbacks and of tropical variability among the different heatwave types. The classification is computed in terms of circulation patterns. By inferring the near‐surface temperature through atmospheric circulation, we aim to identify the predictable component of the heatwave events. All five heatwave circulation patterns are characterized by persistent anticyclonic anomalies located over the region with maximum temperatures. We show that soil moisture deficit is not a required precondition for the occurrence of heatwaves over most of Europe. However, heatwave events over southern Europe exhibit some sensitivity to dry conditions. We use a simplified index to describe the dominant mode of tropical convection at intraseasonal timescales. The index, based on precipitation anomalies, represents the evolution of the Boreal Summer Intraseasonal Oscillation (BSISO). We find that episodes with strong BSISO amplitudes characterized by enhanced convection over India, Bay of Bengal and China sea favour the occurrence of heatwave events over Russia. The results highlight the role of tropical intraseasonal variability in enhancing the predictability of some extreme temperature events over Europe. [ABSTRACT FROM AUTHOR]

This study analyses the spring climate anomalies under different configurations of western North Pacific anticyclone (WNPA) and Siberian high (SH). Compared to the WNPA alone with increased precipitation over southern China, the out‐of‐phase configuration (strong‐WNPA–weak‐SH) can enhance the WNPA‐related southwesterlies over eastern China, favouring increased precipitation with larger amplitude over southern China and southern Northeast China. Differently, the in‐phase configuration (strong‐WNPA–strong‐SH) decreases and confines WNPA‐related southwesterlies over southern China, causing decreased precipitation over eastern Southwest China; additionally, the WNPA‐related southwesterlies converge with SH‐related northwesterlies over the lower reaches of the Yangtze River, causing increased precipitation there. For surface air temperature, the WNPA alone induces warming over western Southwest China through changing heat flux, while the SH alone induces cooling over northern China through changing temperature advection, and over Northwest and southern China through changing heat flux. In the out‐of‐phase configuration, warming expands into Northern China due to the cooperation of weakening SH. In the in‐phase configuration, SH‐related cooling mainly occurs to the north of the Yangtze River because of the obstruction of WNPA. Further analysis indicates the eastern Pacific (EP) and central Pacific (CP) El Niño‐Southern Oscillation (ENSO), European low and Ural high contribute to the different configurations of WNPA and SH. The EP ENSO‐related zonal overturning circulation intensifies the WNPA and European low‐induced warm advection weakens the SH, resulting in the out‐of‐phase configuration. The CP ENSO‐related zonal overturning circulation intensifies the WNPA with a northeast–southwest tilt and Ural high‐induced cold advection intensifies SH, resulting in the in‐phase configuration. [ABSTRACT FROM AUTHOR]

Snowfall and snow accumulation play a crucial role in shaping ecosystems and human activities in the Alpine region. This resource is under threat as a consequence of the visible effects of global warming, and, therefore, it appears urgent to understand how snowfall trends have changed in time and space. In this context, we recovered data from over a hundred snowfall (HN) time series covering the period 1980–2020 over the mountain region of Trentino‐South Tyrol in the northeastern Italian Alps and analysed them to understand snowfall climatology in the region, recent trends and their dependence on elevation and timing of the season. Negative, although not always statistically significant, trends were found in the lowest elevation range (0–1,000 m a.s.l.) over the whole winter season, while some positive and even significant trends were found from January to March above 2,000 m a.s.l. The intermediate elevation range (1,000–2,000 m a.s.l.) exhibits a strong variability with no clear trend. Negative and statistically significant trends were found in April for all elevations. An attribution analysis was performed using precipitation (P), mean air temperature (TMEAN), and large‐scale synoptic descriptors, such as the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) indices. The analysis shows that, overall, P is the driver that best explains the snowfall trends, but, for low elevations, especially during mid‐winter, TMEAN is more relevant. Low elevations are facing a clear decrease in HN due to a significant increase in mean temperatures, while high elevations during mid‐winter display a slight increase in HN, associated with a general increase in precipitation. NAO and AO indices exhibit no significant correlations with HN, except at the lowest elevations and at the beginning of the season. [ABSTRACT FROM AUTHOR]

In this study we analyse the climatic impact over South America due to Amazon deforestation for positive (PDO+) and negative (PDO−) PDO phases. The regional climatic model RegCM4 was run from 1968 to 2003 for the domain including a great area of the Pacific Ocean and South America, using 50 km as spatial resolution. The Amazon deforestation scenario is that one extrapolated to the 2050 year. Under deforestation, the results show precipitation depletion over the extreme northern South America and precipitation increase over areas to the south, including deforested pixels and east‐southern sectors. These changes are more intense and occupy larger areas during the rainy season. Simulations also indicate that deforestation may impose a drier corridor following the Andes Mountains oriental slopes, from northwestern to south‐southeastern South America, in the rainy season. The impact from deforestation in distinct PDO phases presents the greatest signals over central‐southern South America, in a northwest–southeast direction. Over southeast Brazil, deforestation presents a slightly northeastward displacement of the PDO+ positive precipitation anomalies, intensifying the precipitation anomalies in the north sector and weakening over the south sector. The deforestation effects are more intense in the rainy season and have opposite signals between PDO+ and PDO−. Over the southeast South America, deforestation provides a weaker signal on PDO+ negative precipitation than in PDO−. The increased air temperature over the northern South America, near the northern coast, due to the joint effect of deforestation and PDO variability is more evident during PDO+ dry season than PDO− rainy season. This study illustrates the importance of considering long‐term climate simulations to better understand the role of deforestation in the lower frequency variability over South America. [ABSTRACT FROM AUTHOR]

Known as the "roof of the world", 50%–56% area of the Qinghai–Tibet Plateau (QTP) is covered by seasonal frozen ground (SFG), which has an important impact on local and global climate change, terrestrial ecosystems, and regional energy and hydrological cycles. In this study, long‐term observational data of air and soil water (precipitation and soil moisture) and heat [surface air temperature (SAT) and soil temperature (ST)] at 30 meteorological stations were used to study the temporal and spatial changes of SFG and their possible causes for the central‐eastern QTP (CEQTP). The results showed that latitude and altitude are the key factors affecting the spatial distributions of seasonal freeze–thaw activities of CEQTP. The stations with deeper freeze depths and more freeze days are mainly located in high‐altitude and high‐latitude regions, and those with shallower freeze depths and fewer freeze days are mainly located in the low‐altitude and low‐latitude regions of the southern QTP. This may be the reason that latitude and altitude are the key factors determining the temperature distribution on the CEQTP. SAT, ST, precipitation, and soil moisture are all significant correlations with the freeze depth, freeze days, freeze start date (FSD) and thaw end date (TED), and the abrupt change years of them are also consistent; they are the important factors affecting the freeze–thaw changes (FTCs) of SFG. Among them, ST is the key factor influencing the FTCs of SFG, and the variations of monthly average soil temperature (MAST) at 0–320 cm depths are the inverse of those of the monthly average freeze depth and freeze days during the year. Using the MAST data at 0–320 cm depths and the 0°C ST threshold, the soil freeze–thaw processes at different depths on the CEQTP are revealed. Affected by global warming, SAT and ST at different depths on the CEQTP have shown the upward trends since the 1980s. Additionally, precipitation and soil moisture have also increased substantially, especially since the late 1990s. Enhancement of warming and wetting conditions from the land surface to the deep soil have accelerated the thawing of SFG, and led to the delay of FSD and the advance of TED, which further caused the reduction of freeze depth and freeze days of SFG on the QTP, especially since the late 1990s. [ABSTRACT FROM AUTHOR]

In this study, we focus on the features and predictability of winter 2‐m air temperature (t2m) over China under different intensities of La Niña events. The seasonal and intraseasonal prediction skills of winter t2m over China are examined by four state‐of‐art seasonal climate forecast models. The results show that the China winter t2m anomaly is sensitive to the intensity of La Niña. Strong and moderate (S/M) La Niña events are often accompanied by cold winter over China, while a positive anomaly of mean t2m over China frequently occurs in the weak La Niña events. The intensity difference of the East Asian winter monsoon (EAWM) results in this discrepancy. All models have a slightly higher anomaly correlation coefficient (ACC) for the S/M La Niña than that for the weak La Niña in leading 1 month (LT1). Both individual models and MME exhibit higher skills of anomaly sign consistency rate (ASCR) for the S/M La Niña, which implies that models predict t2m anomaly over China more accurately in the S/M La Niña events. Furthermore, the winter t2m over China presents larger intraseasonal variability for the S/M La Niña than that for the weak La Niña. Models can capture the t2m anomaly over China more accurately when the temperature varies gently with small intraseasonal variability during winter for the S/M La Niña. [ABSTRACT FROM AUTHOR]

This paper describes the construction of a 0.25° × 0.25° daily soil temperature (TS) data set at six soil depths (i.e., 0, 5, 10, 15, 20, and 40 cm) based on a homogenized TS at more than 2,000 meteorological stations across mainland of China for 1960–2017. First, at each station, the observed TS is split into a daily climatology of 1981–2010 and a daily anomaly for 1960–2017. Then, they are respectively interpolated to 0.25° × 0.25° horizontal resolution by using the thin‐plain spline and the angular distance weight interpolation methods. Finally, a gridded multilayer TS data set is constructed from the sum of the above two gridded products. Latitude, longitude, and high‐resolution digital elevation models are explicitly incorporated into the interpolation processes. Selectivity tests indicate that the optimal interpolation distance for the TS anomaly field is 500 km, where the gridded data set may have maximum coverage of the entire Chinese mainland. The results show that the gridded TS climatology is slightly lower than the station observations. The differences between gridded and the station‐based TS varies ±3°C, with relatively large in SW and TIBET. For the gridded anomaly fields, the cross‐validation shows that the mean absolute errors between gridded and station‐based TS are generally less than 0.6°C in the YZ, SW, and SC, but they are above 1.0°C in the NW and TIBET due to the sparse station distribution and large topographic relief. From 1961 to 2016, the seasonal variability of the gridded TS is generally consistent with that of the surface air temperature. The gridded TS over the majority of land displays significantly positive trends in both summer and winter (p > 95%), indicating an overall warming land surface in China. The long‐term gridded TS data not only preserves the spatial features represented in the station observations, but also provides a spatio‐temporal continuous long‐term product of mainland China. Thus, it may be used in future studies, for example, evaluation of land surface model simulations, validation of satellite retrievals, land‐atmosphere interactions, and climate change. [ABSTRACT FROM AUTHOR]

The onset of periods with very high or low air temperatures has aroused general interest for a long time, which is understandable since it has many dangerous effects directly affecting humans. Such perilous climatic phenomena include exceptionally cold and exceptionally warm months, which this study investigates for all Europe and for its five physico‐geographic regions over the 190‐year period of 1831–2020. Therefore, the research in this paper includes two periods characteristic of the history of climate – the late Little Ice Age (LIA) and present‐day warming. The studies are based on average monthly air temperature values from 40 weather stations in Europe. In this paper, exceptionally cold months (ECMs) or exceptionally warm months (EWMs) are considered to have occurred when the average air temperature at a station differed from the respective long‐term average by at least two standard deviations. The highlights of the study include the identification of a drop in the number of ECMs by 20 over the entire 190‐year period, and a highly statistically significant increase in EWMs by 44 between 1980 and 2020. These changes proceeded with different intensities from one physico‐geographic region of Europe to another. [ABSTRACT FROM AUTHOR]