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This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation. [ABSTRACT FROM AUTHOR]

This article addresses the simulation of urban air temperatures with a focus on evaluating the Urban Weather Generator (UWG) model over Toulouse, France. As urban temperatures, influenced by factors like urbanization, anthropogenic heat release, and complex urban geometry, exhibit an urban heat island (UHI) effect, understanding and mitigating UHI become crucial. With increasing global warming and urban populations, aiding urban planners necessitates accurate simulations requiring data at the canyon level. The paper evaluates UWG's performance in simulating air temperatures under realistic conditions, emphasizing an operational context and a non-specialist user's perspective. The evaluation includes selecting the most suitable meteorological station, assessing the impact of the rural station choice, and conducting a sensitivity analysis of input parameters. The validation demonstrates good agreement, with a mean bias error (MBE) of 0.02 °C and a root mean square error (RMSE) of 1.73 °C. However, we highlight the fact that UWG performs better in a densely urbanized area, and exhibits limitations in sensitivity to urban surface parameter variations, particularly in less urbanized areas. [ABSTRACT FROM AUTHOR]

The Photovoltaic Geographical Information System (PVGIS) is a web application that provides free access to solar radiation and temperature data, typical meteorological year (TMY) data, and to photovoltaic performance assessment tools for any place in most parts of the world. The PVGIS was originally developed over 20 years ago, and since then, it has been under continuous development. At present, there are two versions of the PVGIS online—the older version 5.1 and the newest version 5.2. PVGIS 5.2 includes substantial improvements compared to the previous version, e.g., the update of the underlying data sets both in terms of quality, resolution, and geographical coverage and the extension of the time period used. This paper focuses on comparing the TMYs (and more specifically the TMY time series of air temperature), coming from both PVGIS 5.1 and 5.2, with the TMY produced on the basis of ground station meteorological data and with the ground station data itself. The results show that whereas overall the errors and biases for most locations are within the expected range (mean stationRMSE 4.27), these differences increase in places with complicated topography, e.g., in the Alps (maximum stationRMSE 9.50). [ABSTRACT FROM AUTHOR]

Meteorological drought (MDr) causes considerable economic losses in many countries, including in relation to agriculture. To examine the diversity of seasonal meteorological drought in Poland, the study uses monthly precipitation sums in the period from March to November, collected from 74 ground-based meteorological stations in 1951–2020. The paper defines meteorological drought on the basis of the standardized precipitation index in three seasons (SPI-3) and differentiates three degrees of drought intensity: extreme, severe and moderate. The study also calculates the size of Poland's area affected by meteorological drought (AAMDr) and determines the relationship between AAMDr and SPI-3. MDr in Poland occurred more frequently in spring and autumn (every 4–5 years) than in summer (every 7 years). In the areas affected by extreme drought, precipitation was below the average, mainly ≤50% of the climatic norm, and air temperature values were below or above the average of the climatic norm, mainly ranging from −1.0 to 1.0 °C. A significant negative correlation between AAMDr and SPI-3 indices was found. The obtained results could be useful for managing climatic risk and developing regional and local agriculture adaptation plans aimed at mitigating the effects of climate change. [ABSTRACT FROM AUTHOR]

The paper compares air and sea surface temperatures in recent years on two islands in the Adriatic Sea. The data measured at the climatological station Krk on the island of Krk and the main meteorological station Lastovo on the island of Lastovo are used. The island of Krk is located in the north of the Adriatic Sea and Lastovo in the south. Since a significant increase in air and sea surface temperatures has been observed over the last thirty years, the goal is to establish how they reflect at these two stations, 313 km apart. The goal of the analysis is to monitor the changes in these two islands to reduce the negative impacts they may cause. The analysis of sea temperatures showed that global warming has a greater impact in the northern Adriatic than in the southern Adriatic. Air and sea surface temperatures have a faster upward trend on Krk than on Lastovo. Similar to the Mediterranean Sea, a positive trend was observed in the Adriatic Sea for both sea surface temperature and air temperature. [ABSTRACT FROM AUTHOR]

The study of climate element distribution models under complex terrain conditions is a popular topic in the field of GIS application in climatology, especially in plateau areas with a complex topography and scarce meteorological station information. In this paper, the spatial and temporal heterogeneity of temperature and precipitation at the northeastern edge of the Tibetan Plateau was analyzed by taking the northeastern edge of the plateau as the study area and constructing a topographic spatial statistical model using 47 meteorological stations and digital elevation models from 1981 to 2010. The following conclusions were drawn from the study: (1) The ME of the temperature distribution model for each month is below 0.9 °C; the maximum ME of the precipitation distribution model for each month is −5.808 mm in July, and the precipitation distribution model has similar error characteristics with the temperature distribution model, which can reflect the horizontal zone distribution pattern of meteorological data and can clearly show the changes of temperature and precipitation as the altitude increases. (2) The spatial distribution pattern of temperature is as follows: the temperature in the study area gradually increases from the southwest to the northeast, with Zhouqu County in Linxia Prefecture and Gannan Prefecture as the main high-temperature areas; the spatial distribution of precipitation is as follows: the precipitation in the southwest of the study area is significantly higher than that in the north, and the precipitation in Linxia Prefecture is significantly lower than that in Gannan Prefecture. (3) The temporal distribution pattern of the temperature distribution model is as follows: the overall temperature in the study area is at its lowest level in January, and the maximum temperature is only 2.6 °C, until July, when the maximum temperature rises to 24.2 °C and then gradually starts to decline; the spatial distribution of precipitation is as follows: the precipitation in the study area gradually rises from January, and the maximum precipitation rises to July and then starts to decline, and in December the precipitation falls to the lowest level. The temporal distribution characteristics of the precipitation distribution model are similar to those of the air temperature model, with obvious hydrothermal synchronization characteristics. [ABSTRACT FROM AUTHOR]

Near-land surface air temperature (NLSAT) is an important meteorological and climatic parameter widely used in climate change, urban heat island and environmental science, in addition to being an important input parameter for various earth system simulation models. However, the spatial distribution and the limited number of ground-based meteorological stations make it difficult to obtain a large range of high-precision NLSAT values. This paper constructs neural network, long short-term memory, bi-directional long short-term memory, support vector machine, random forest, and Gaussian process regression models by combining MODIS data, DEM data, and meteorological station data to estimate the NLSAT in China's mainland and compare them with actual NLSAT observations. The results show that there is a significant correlation between the model estimates and the actual temperature observations. Among the tested models, the random forest performed the best, followed by the support vector machine and the Gaussian process regression, then the neural network, the long short-term memory, and the bi-directional long short-term memory models. Overall, for estimates in different seasons, the best results were obtained in winter, followed by spring, autumn, and summer successively. According to different geographic areas, random forest was the best model for Northeast, Northwest, North, Southwest, and Central China, and the support vector machine was the best model for South and East China. [ABSTRACT FROM AUTHOR]

Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP. [ABSTRACT FROM AUTHOR]

Knowledge of the flowering dates of black locust trees (Robinia pseudoacacia L.) is crucial information of both economic significance and scientific interest. Black locust is in fact an excellent melliferous plant and can provide information on climate change impacts due to its large distribution throughout the world as planted and naturalized trees. This paper presents the calibration and validation of a phenological model targeted at the simulation of the whole process of black locust flowering (from BBCH 51—inflorescence of flower buds visible—to BBCH 69—end of flowering). The work relies on the phenological observations gathered in the context of IPHEN, the Italian PHEnological Network, with a broad latitudinal distribution of the observational sites (from 37.53° to 46.28° N) and a long time span, from 2010 to 2021. Phenology modeling is based on the Normal Heat Hours approach, which translates air temperature into thermal units based on a plant specific response curve. As meteorological input data, a high resolution (0.045°) gridded dataset was obtained by spatial interpolation of GSOD (NOAA) weather stations. [ABSTRACT FROM AUTHOR]

The paper presented the results of surface water temperature measurements from 17 lakes located in northern Poland and Belarus over the period of 50 years (1971–2020). Using the Excel computer program, Corel Quattro Pro 8, and graphic Draw 9, annual and monthly averages, extreme values, and annual and monthly trends were calculated and presented. These were supplemented by the monthly average air temperatures for 13 weather stations. The research showed that the temperature in all lakes was characterized by a positive trend at the level of 0.044 °C year−1. The largest lake was characteristic for the Chervonoe Lake—0.066 °C year−1—and the smallest 0.029 °C year−1 for the deep Lake Hańcza. In the course of the average monthly surface water temperatures, a positive trend was also observed, ranging from 0.015 °C year−1 in January to 0.069 °C year−1 in May. These values correlated with the average air temperatures in the winter months (December–March) and with the winter NAO index (DJFM). [ABSTRACT FROM AUTHOR]

Fruits, garden plants, and agricultural crops grown in Poland exhibit wide variations in their sensitivity to frost, particularly in early spring. In the case of frost, generally, the yield and quality are reduced, and sometimes, entire plants can be destroyed. This article characterizes the occurrence of ground frosts (at 5 cm agl) and air frosts (at 200 cm agl) in Poland gathered from 52 meteorological stations affiliated with IMGW-PIB between 1971 and 2020. To assess the real risk of frost to plants, the variability of this phenomenon was analyzed per thermal growing season (defined as air temperature >5 °C), rather than in traditional calendar terms as presented in most studies. In the climatic conditions of Poland, the growing season is characterized by a reported 28 days with ground frost and 13.3 days with air frost, approximately. In spring, the last ground frost disappears, on average, on a country scale, on May 14, and air frost on April 27. In turn, in autumn, the first ground frost is recorded, on average, on 1 October and air frost on 14 October. On the basis of the selected characteristics of frost and the growing season, four areas of potential risk of ground and air frost in the growing season, as well as in spring, were determined with the use of cluster analysis. [ABSTRACT FROM AUTHOR]

The temporal variability of air temperature in the Middle Volga region from 1828 to 2021 is considered according to instrumental observations at the oldest meteorological station in the east of the East European Plain (Kazan University) and throughout the Asian part of Russia against the background of long-term climate fluctuations in the Northern Hemisphere of Earth. A general trend toward an increase in air temperature was revealed. It was found that climate change in Kazan was consistent with the climatic processes that occurred in the Middle Volga region as a whole. The greatest warming for the entire observation period was observed in the winter and spring seasons of the year. In December, warming occurred at a maximum rate of 0.28 °C/10 years. At the same time, the most intense warming process was observed from 1991 to 2021. The analysis of low-frequency fluctuations in the series of monthly average air temperatures made it possible to identify different periods of change, both in type (direction) and intensity. It is shown that in the Middle Volga region, positive anomalies of air temperature have occurred more often than negative ones in recent decades. Statistical data processing was also carried out for 30-year periods, starting from the first period, i.e., 1841–1870. This made it possible to reveal long-term changes in air temperature. Comparisons of climatic parameters in two periods, i.e., 1828–1945 and 1946–2021, allowed us to reliably detect the climatic beginning of the increasingly identifiable Anthropocene epoch (since 1946), characterized by a sharp increase in air temperature, increased interannual variability of the air temperature regime, and a significant increase (by about three times) in the rate of warming in the Middle Volga region. A correlation was made between atmospheric circulation indices and air temperature fluctuations in Kazan over different periods. The closest relationship was found for the 1990–2020 period. It is shown that the contribution of global factors to air temperature variability in the Middle Volga region during the Anthropocene reached 37% in winter and 32% in summer; in annual terms, this contribution amounted to 54%. [ABSTRACT FROM AUTHOR]

Mountains are highly sensitive to the effects of climate change, including extreme short- and long-term weather phenomena. Therefore, in spite of relatively high annual precipitation totals, mountains might become endangered by droughts. The paper presents drought trends in the Polish Carpathians located in Central Europe. Data from the period 1991–2020 from 12 meteorological stations located in various vertical climate zones of the mountains were used to define drought conditions using the following indices: Standardized Precipitation (SPI), Standardized Precipitation Evapotranspiration (SPEI), Relative Precipitation (RPI) and Sielianinov. Additionally, four forest drought indices were used in order to estimate the impact of drought on beech as a typical Carpathian tree species, i.e., the Ellenberg (EQ), Forestry Aridity (FAI), Mayr Tetratherm (MT) and De Martonne Aridity (AI) indices. Statistically significant but weak trends were obtained for the 6-month SPI for four stations (indicating an increase in seasonal to mid-term precipitation), for the 1-month SPEI for three stations, for the 3-month SPEI for four stations, and for MT for all stations (indicating an increase in drought intensity). The analysis of dry month frequency according to particular indices shows that at most of the stations during the last decade of the study period, the frequency of dry months was much higher than in previous decades, especially in the cold half-year. Two zones of the Polish Carpathians are the most prone to drought occurrence: the peak zone due to the shift in climatic vertical zones triggered by the air temperature increase, and the forelands and foothills, together with basins located about 200–400 m a.s.l., where the mean annual air temperature is the highest in all the vertical profile, the annual sums of precipitation are very diversified, and the conditions for beech are already unfavorable. [ABSTRACT FROM AUTHOR]

Extreme rainfall is the main contributing factor to landslides. Therefore, it is of great significance to monitor and forecast short-term rainfall in landslide-prone areas. However, the spatial scale of landslide-prone areas is small, and traditional numerical forecast models have difficulty in accurately forecasting rainfall on this scale. To solve the above problem, this study proposes a short-term rainfall forecasting method for landslide-prone areas by combining the back-propagation neural network (BP-NN) algorithm and global navigation satellite system (GNSS) observations to achieve accurate short-term rainfall forecasting in landslide-prone areas. Firstly, a high-precision atmospheric weighted-average temperature (Tm) model is established using radiosonde data to obtain high-precision precipitable water vapor (PWV) estimates. Secondly, the BP-NN algorithm is introduced, and the GNSS-derived PWV, temperature and pressure from a meteorological station, and rainfall for the previous and next hour are used as input parameters to establish a BP-NN-based rainfall forecast model. As an illustrative case, experiments are conducted in a landslide-prone area in Yunnan Province using data from 15 GNSS stations and the corresponding meteorological station. Statistical results show that the established regional Tm model has high accuracy, with an average root mean square (RMS) and bias of 3 K and 0.15 K, respectively. In addition, the short-term rainfall forecast model based on the BP algorithm achieves a true detection rate of up to 93.70% and a false forecast rate of as low as 38.30%, which is significant for short-term rainfall forecasting in landslide-prone areas. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is the first step in calculating crop irrigation demand, and numerous methods have been proposed to estimate this parameter. FAO-56 Penman–Monteith (PM) is the only standard method for defining and calculating ET0. However, it requires radiation, air temperature, atmospheric humidity, and wind speed data, limiting its application in regions where these data are unavailable; therefore, new alternatives are required. This study compared the accuracy of ET0 calculated with the Blaney–Criddle (BC) and Hargreaves–Samani (HS) methods versus PM using information from an automated weather station (AWS) and the NASA-POWER platform (NP) for different periods. The information collected corresponds to Module XII of the Lagunera Region Irrigation District 017, a semi-arid region in the North of Mexico. The HS method underestimated the reference evapotranspiration (ET0) by 5.5% compared to the PM method considering the total ET0 of the study period (26 February to 9 August 2021) and yielded the best fit in the different evaluation periods (daily, 5-day mean, and 5-day cumulative); the latter showed the best values of inferential parameters. The information about maximum and minimum temperatures from the NP platform was suitable for estimating ET0 using the HS equation. This data source is a suitable alternative, particularly in semi-arid regions with limited climatological data from weather stations. [ABSTRACT FROM AUTHOR]

The aim of this research was to determine relationships between solar activity and variability of discharges of three Central European rivers: the Vistula, Odra and Warta in Poland in the multi-annual period of 1901–2020. Changes in precipitation and air temperature at Poznań meteorological station in the same period were also analyzed. The long-term variations in river runoff were investigated both from the point of view of temporal variability in relation to climate variations in the study area, and from the point of view of linear/non-linear links to solar activity, as described by the Wolf sunspot number. The wavelet transform analysis was used to highlight the frequency-time distribution of the coherences between solar and discharge variability. It was found that most of the links between solar activity and discharges were non-linear. [ABSTRACT FROM AUTHOR]

Analysis of a long-term temperature variability of mountains has been neglected for a long time. Here we homogenised and reconstructed four temperature series (1961–2020) of selected mountain stations situated above 1000 m representing the area of northern Moravian mountain ridges (the Czech Republic) and their spatiotemporal variability was examined. A statistically significant increase of 10-year linear trends of annual (0.26–0.38), summer/winter half-year and seasonal mean temperatures were found at most stations. Summer (JJA) was detected as the season with the highest mean temperature increase between 1961–1990 and 1991–2020 normal periods (1.5 °C). From the spatial point of view, the climate conditions were becoming warmer faster in higher elevations compared to the lower ones. At all stations, a statistically significant decrease in ice and frost days was observed (−9.1–19.9 and −9.5–16.3, respectively). Moreover, it was proved that the temperature series of a newly established Vysoká hole station are representative enough and comparable with measurements of other meteorological stations in the studied region. This research provided valuable insight into the temperature variability of mountain ridges and the results completed our sparse knowledge about temperature changes in the mountain regions in Central Europe. [ABSTRACT FROM AUTHOR]

Roof surfaces on which air conditioning equipment is installed cause significant air temperature increases around the condensers due to roof thermal properties, resulting in excess electrical energy consumption and greenhouse gas (GHG) emissions. An experiment to quantify such excesses during the summer was conducted at a university campus in Mexicali, Mexico. The air temperatures and surface temperatures for three types of roofs were recorded. Temperatures (condenser inlet air temperatures) were used as the input data to a priori estimate the electrical consumption of a 5-ton A/C equipment working over these different roofs. Temperatures recorded by a nearby meteorological station were used as a reference. The results indicate a differential of up to 4.81 °C, resulting in an excess of electricity consumption of 5.55 kWh and an additional 3.9 kg of CO2 emissions, representing an 8.2% energy surplus and differences of up to 2.1% in electricity consumption provoked by microclimate. [ABSTRACT FROM AUTHOR]

It is of great importance to study the changes in reference evapotranspiration (ET0) and the factors that influence it to ensure sustainable and efficient water resource utilization. Daily ET0 data calculated using the Penman–Monteith method from 37 meteorological stations located within Guangdong Province in the humid zone of southern China from 1960 to 2020 were analyzed. The trend analysis and Mann–Kendall test were used to analyze the time series changes in ET0 and major climatic factors (air temperature (T), relative humidity (RH), sunshine duration (SD), and wind speed (u2)) for over 61 years. Sensitivity and contribution analyses were used to evaluate the driving factors of ET0. The main findings of the study are as follows: (1) the trend in average annual ET0 time series in Guangdong slightly increased at a trend rate of 1.61 mm/10a over the past 61 years, with most stations experiencing an increase in ET0. During the same period, air temperature significantly increased, while RH and SD decreased; u2 also decreased. (2) Sensitivity analysis showed that ET0 was more sensitive to RH and T than SD and u2, with ET0 being most sensitive to RH in spring and winter and T in summer and autumn. (3) The contribution analysis showed that T was the dominant factor for ET0 variation in Guangdong, followed by SD. SD was found to be the dominant factor in ET0 changes in areas where the "evaporation paradox" occurred, as well as in spring and summer. The study concludes that the climate in Guangdong became warmer and drier over the past 61 years, and if the current global warming trend continues, it will lead to higher evapotranspiration and drought occurrence in the future. [ABSTRACT FROM AUTHOR]

Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between the predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques. [ABSTRACT FROM AUTHOR]

A new satellite-enabled interoperable service has been developed to provide high spatiotemporal and continuous time series of Growing Degree Days (GDDs) at the field. The GDDs are calculated from MSG-SEVIRI data acquired by the EUMETCast station operated by IAASARS/NOA and downscaled on-the-fly to increase the initial coarse spatial resolution from the original 4–5 km to 1 km. The performance of the new service SENSE-GDD, in deriving reliable GDD timeseries at dates very close to key phenological stages, is assessed using in situ air temperature measurements from weather stations installed in Gerovassiliou Estate vineyard at Epanomi (Northern Greece) and an apple orchard at Agia (Central Greece). Budburst, pollination, and the start of veraison are selected as key phenological stages for the vineyards, whilst budburst and pollination for the apple orchard. The assessment shows that SENSE-GDD provided uninterrupted accurate measurements in both crop types. A distinct feature is that the proposed service can support decisions in non-instrumented crop fields in a cost-effective way, paving the way for its extended operational use in agriculture. [ABSTRACT FROM AUTHOR]

Due to the influence of complex factors such as atmospheric dynamic processes, physical processes and local topography and geomorphology, the prediction of near-surface meteorological elements in the numerical weather model often has deviation. The deep learning neural networks are more flexible but with high variance. Here, we proposed a stacking ensemble model named FLT, which consists of a fully connected neural network with embedded layers (ED-FCNN), a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to overcome the high variance of a single neural network and to improve prediction of maximum air temperature. The case study of daily maximum temperature forecast evaluated with observation of almost 2400 weather stations shows substantial improvement over that of single neural network model, ECMWF-IFS and statistical post-processing model. The FLT model can more effectively improve the forecast bias of the ECMWF-IFS model than that of any of the above single neural network model, with the RMSE reduced by 52.36% and the accuracy of temperature forecast increased by 43.12% compared with the ECMWF-IFS model. The average RMSEs of the FLT model decreases by 8.39%, 1.50%, 2.96% and 16.03%, respectively, compared with ED-FCNN, LSTM, TCN and the decaying average method. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the whole urban area together with rural zone have averaged 1.9 and 0.9 °C, respectively, in recent years. The UHI in Moscow has stabilized over the past decade and is not growing. Under conditions of a strong anticyclone, the maximal UHI intensity in space and time reaches 11–12 °C. Low cloudiness and amplitudes of diurnal air temperature, as well as surface temperature, demonstrate the closest relationship with the UHI intensity among other parameters with the correlation coefficient of up to −0.67 for low cloudiness and the maximal UHI intensity. The effect of wind speed, total cloudiness and relative humidity on the UHI is slightly weaker, but still significant. The relationships of all meteorological parameters with the maximal UHI intensity are closer than those with the average one. The multiple correlation coefficient between the maximal UHI intensity and both parameters (low cloudiness and average daily wind speed) is 0.76–0.82. The UHI intensity function of air temperature has a minimum in the range from −4 to 0 °C; its growth both at lower and higher T is due to the influence of anticyclonic weather. The UHI intensity function of wind speed decreases with wind strength. The threshold value at which this function asymptotically approaches its lower limit is 10 m/s in the 40–200 m air layer. The UHI intensity functions of both total and low cloudiness decrease with increasing cloudiness and the differences between them are significant if the cloud cover is more than 50%. [ABSTRACT FROM AUTHOR]

The ability to produce high-resolution climate maps is crucial for assessing climate change impacts and mitigating climate disasters and risks in developing countries. Mainstream geostatistical downscaling techniques use spatial interpolation or multi-linear regression models to produce high-resolution climate maps in data-scarce regions. Since global climate evolution is a nonlinear process governed by complex physical principles, these linear downscaling techniques cannot achieve the desired accuracy. Moreover, these techniques cannot utilize different resolution data as model inputs. In this study, we developed a hybrid of multilayer perceptrons that could couple high-resolution topographic data with sparse climate observation data well and then generate high-resolution climate maps. To test the performance of our tool, we generated high-resolution precipitation and air temperature maps using sparse observation data from 21 meteorological stations in Ethiopia. The accuracy of the high-resolution climate maps generated using our hybrid of MLPs clearly outperformed those using a multi-linear regression model or a pure MLP. [ABSTRACT FROM AUTHOR]

This research examined the characteristics of cold days and spells in Bangladesh using long-term averages (1971–2000) of maximum (Tmax) and minimum temperatures (Tmin) and their standard deviations (SD). Cold days and spells were calculated and their rate of change during the winter months (December–February) of 2000–2021 was quantified. In this research, a cold day was defined as when the daily maximum or minimum temperature is ≤−1.5 the standard deviations of the long-term daily average of maximum or minimum temperature and the daily average air temperature was equal to or below 17 °C. The results showed that the cold days were more in the west-northwestern regions and far less in the southern and southeastern regions. A gradual decrease in cold days and spells was found from the north and northwest towards the south and southeast. The highest number of cold spells (3.05 spells/year) was experienced in the northwest Rajshahi division and the lowest (1.70 spells/year) in the northeast Sylhet division. In general, the number of cold spells was found to be much higher in January than in the other two winter months. In the case of cold spell severity, Rangpur and Rajshahi divisions in the northwest experienced the highest number of extreme cold spells against the highest number of mild cold spells in the Barishal and Chattogram divisions in the south and southeast. While nine (out of twenty-nine) weather stations in the country showed significant trends in cold days in December, it was not significant on the seasonal scale. Adapting the proposed method would be useful in calculating cold days and spells to facilitate regional-focused mitigation and adaptation to minimize cold-related deaths. [ABSTRACT FROM AUTHOR]

To minimize the impact of snowfall and ice formation on safety of transportation, salt is sprinkled on the asphalt every winter. However, the use of salt has economical as well as ecological disadvantages. To resolve these problems, road heating systems are used in the northern regions of Europe and America. Despite their widespread usage, considerable potential of the operational optimization is evident. The current systems are controlled under predefined weather conditions such as start of operation at 5 °C air temperature, even when snowfall is absent. Consequently, loss of energy input to heat the system is caused. To avoid unnecessary financial and energetic expense, this study presents CFD-based performance investigation as a basis for a novel predictive controller to increase the operational efficiency of hydronic road heating systems (HRS). The simulation model was developed based on a real operational HRS located in Ingolstadt and composed of bridges and ramps for a total surface of 1989 m2. Climate data of the years 2019–2020 from local weather stations were implemented in the simulation model for performance prediction on extreme climate conditions. This investigation identified that up to 70% of operational hours in terms of energy input can be saved by using a hypothetical predictive controller, thus making the HRS a more economically efficient and environmentally attractive alternate to conventional de-icing techniques. [ABSTRACT FROM AUTHOR]

The article presents the results of a study on the assessment of modern space–time trends of extreme values of air temperature and precipitation in 42 meteorological stations throughout Kazakhstan for the period from 1971 to 2020. Spatial and temporal analysis of the distribution of specialized climatic indices was recommended by the WMO climatology commission and an assessment of their trends was carried out. Spatial heterogeneity was revealed in terms of the degree of manifestation of changes and trends. Temperature indices are shown to confirm the overall warming trend. The division of the territory of Kazakhstan by the degree of manifestation of climate change into the southwestern and northeastern half was revealed. Extreme trends are most pronounced in the southwestern half, where a significant trend has been identified both for an increase in extremely high daytime and extremely low night temperatures. The calculated trends in temperature indices are generally significant, but the significance is mainly not ubiquitous; the trends are significant only in certain parts of Kazakhstan. WSDI and CSDI trends were found to confirm a widespread increase in the overall duration of heat waves and a reduction in the overall duration of cold waves. No significant extreme effects were found in the sediments. It is confirmed that Kazakhstan has weak, statistically insignificant, positive and negative trends in the maximum duration of the non-traveling period. Precipitation index trends, unlike temperature ones, are statistically insignificant in most of the country. [ABSTRACT FROM AUTHOR]

Short-term fluctuations in air temperature, called the daily temperature range (DTR), or its daily amplitude, have a strong impact on ecosystems, as well as on the health and well-being of people. The pronounced effect of DTR on mortality from all causes and especially on cardiovascular mortality is well documented in the scientific literature, but little is known about spatial, inter-annual, and inter-seasonal fluctuations of DTR in the Russian Far East (RFE), an area with high annual dynamics of air temperature. Data from 99 weather stations for the period from 2000 to 2019 was used to evaluate spatial and temporal DTR patterns at the RFE. A higher DTR, up to 16 °C in Toko on the border with Sakha (Yakutia) as a mean for the entire period, is typical for continental areas further to the north. Lower values are observed at continental weather stations in the south and in coastal climates (4.7 °C in Mys Alevina, Magadan Region). In general, a distinct seasonal change in DTR was found for continental locations with a significant difference from month to month throughout the year. On the annual cycle, the maximum DTR at the continental northern stations is observed in April and June–July, and at the continental southern areas in February; the minimum DTR is shown in November and December. The DTR ranges as much as from 6 °C in December to 17 °C in April in the continental Korkodon in the far north. Locations with a marine climate are characterized by a smoothed seasonal change in DTR, with obvious peaks in February–March and October, and a minimum in July–August. The downward trend in DTR for the period from 2000 to 2019, up to −0.7 °C in coastal Bolsheretsk, is based on a faster increase in the minimum daily temperature compared to the maximum, which is typical for most weather stations at the RFE and is known worldwide as a diurnal asymmetry of global warming. At the same time, an increase in the daily amplitude of air temperature (up to +0.6 °C in continental Dolinovka) was found for some localities, associated with a higher positive trend of maximum temperatures, which contradicts global patterns. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations. [ABSTRACT FROM AUTHOR]

An analysis of spatiotemporal variability of air temperature and precipitation in the Volga Federal District (European Russia) between 1966 and 2021 was carried out. Based on data from 20 meteorological stations, relatively evenly located on the territory under consideration, the spatial distribution of average monthly and average annual air temperatures and monthly and annual precipitation was assessed; some indicators of the temporal variability of these variables in the period under consideration were calculated and analyzed. It was revealed that throughout the Volga Federal District, there was a tendency of climate warming in all months, and a slight increase in annual precipitation, except for the southeast of the district, where the precipitation trend was negative. It is noted that in the period 1955–1998, the number of negative air temperature anomalies was approximately equal to the number of positive ones; however, in the later period 1999–2021, the number of positive anomalies significantly exceeded the number of negative ones. Based on reanalysis data, climatic maps of vaporization and runoff in the Volga Federal District during 1966–2021 were created. The dependence of air temperature fluctuations on the nature of atmospheric circulation was revealed using the NAO, AO, and SCAND indices. On the example of the central part of the district (Republic of Tatarstan), some increase in summer aridity of the climate was revealed by using Budyko's dryness index, Selyaninov's hydrothermal coefficient, and Sapozhnikov's humidification coefficient. The indicators of runoff and evaporation were also calculated using the methods of Schreiber and Ivanov. Against the background of the positive trend in vaporization rates, favorable conditions for a decrease in runoff were noted. [ABSTRACT FROM AUTHOR]

The air temperature trends measured at the central meteorological station Varaždin and the water temperature measured at the Botovo station on the Drava River were analyzed from 1 January 1969 to 31 December 2021. Analyses were performed for three different time scales: year, month, and day. Mann–Kendall testing statistically determined the significant trends over the analyzed period and found increasing air and water temperatures. From 1975 to 1989, three reservoirs of different volumes and water surface areas were built. The Botovo water measuring station is 11 km from the third largest reservoir and 28 km from the mouth. Applying the day-to-day (DTD) method, we determined that the variations in the daily air temperatures are significantly higher than the simultaneous variations in the daily water temperatures. Also, the rise in water temperatures at the downstream water measuring station Botovo was influenced by the construction of reservoirs. The commissioning of the second reservoir in 1982 caused a significant rise in water temperature at the Botovo station. Trends in water temperature increase during all months of the year were statistically significant, while air temperature trends were statistically significant during the warm parts of the year. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is of great significance in studies of hydrological cycle, agricultural water resources, and hydrometeorology. The present study collected daily meteorological data at 536 meteorological stations in China from 1984 to 2019, calculated daily ET0 using the FAO Penman–Monteith equation, analyzed the spatial distribution and temporal variation characteristics of ET0 and meteorological variables at four different spatial scales (continental, regional, provincial, and local), and discussed the sensitivity of ET0 to the meteorological variables and the contribution rates of the meteorological variables to the ET0 variations. The results showed that ET0 increased at 406 out of the 536 stations (75.7%), with the trends being significant at 65 stations at the 5% significance level, and 147 at the 1% significance level. The slope calculated using Sen's method and linear trend method showed that the annual ET0 at the continental scale increased by approximately 12 mm/decade. Most of the stations showed decreasing trends in relative humidity (Hm), sunshine duration (SD), and wind speed at 2 m height (U2) while increasing trends in the maximum air temperature (Tmax) and minimum air temperature (Tmin). ET0 was most sensitive to Hm (sensitivity coefficient, St = −0.66), followed by Tmax (St = 0.29), SD (St = 0.18), U2 (St = 0.16), and Tmin (St = 0.07). Most of the stations showed increasing trends in St for Hm (56.16%), Tmax (72.95%), Tmin (87.31%), and U2 (90.49%), and decreasing trends for SD (69.78%). The variations in Hm, Tmax, and Tmin increased the ET0 at most of the stations (82.28%, 98.13%, and 69.03%, respectively). The variations in SD and U2 decreased ET0 at most of the stations (66.04% and 56.34%, respectively). Some ET0 characteristics in a few regions can be well described using a single spatial scale. However, most regions exhibited significantly different ET0 characteristics across spatial scales. The results of this project can provide reference for hydrological analysis and agricultural water management under climate change conditions and provide data and information for other hydrology-related applications. [ABSTRACT FROM AUTHOR]

The change in the spatial distribution of agroclimatic conditions based on the sum of active temperatures (SAT), growing degree days (GDD), and latitude–temperature index (LTI) is discussed in this article. Data from 20 meteorological stations of IMGW-PIB (Institute of Meteorology and Water Management—National Research Institute) in Poland from the years 1966–2020 were used. The temporal and spatial diversity of mean air temperature and the chosen indices were analyzed for the period from April to October. Designating areas of diverse thermal conditions with respect to plant comfort on the basis of agroclimatic indices was attempted, together with mean air temperature and its temporal changes. The clustering, using the Ward's method, yielded four regions with different thermal resources in Poland. The study period showed an increase in the values of all agroclimatic indices and air temperature during the growing season, suggesting an increase in the thermal resources in the territory of Poland. [ABSTRACT FROM AUTHOR]

As a sensitive indicator for climate change, the spring phenology of alpine grassland on the Qinghai–Tibet Plateau (QTP) has received extensive concern over past decade. It has been demonstrated that temperature and precipitation/snowfall play an important role in driving the green-up in alpine grassland. However, the spatial differences in the temperature and snowfall driven mechanism of alpine grassland green-up onset are still not clear. This manuscript establishes a set of process-based models to investigate the climate variables driving spring phenology and their spatial differences. Specifically, using 500 m three-day composite MODIS NDVI datasets from 2000 to 2015, we first estimated the land surface green-up onset (LSGO) of alpine grassland in the QTP. Further, combining with daily air temperature and precipitation datasets from 2000 to 2015, we built up process-based models for LSGO in 86 meteorological stations in the QTP. The optimum models of the stations separating climate drivers spatially suggest that LSGO in grassland is: (1) controlled by temperature in the north, west and south of the QTP, where the precipitation during late winter and spring is less than 20 mm; (2) driven by the combination of temperature and precipitation in the middle, east and southwest regions with higher precipitation and (3) more likely controlled by both temperature and precipitation in snowfall dominant regions, since the snow-melting process has negative effects on the air temperature. The result dictates that snowfall and rainfall should be concerned separately in the improvement of the spring phenology model of the alpine grassland ecosystem. [ABSTRACT FROM AUTHOR]

Glaciers and snow in the Caucasus are major sources of runoff for populated places in many parts of this mountain region. These glaciers have shown a continuous area decrease; however, the magnitude of mass balance changes at the regional scale need to be further investigated. Here, we analyzed regional changes in surface elevation (or thickness) and geodetic mass balance for 1861 glaciers (1186.1 ± 53.3 km2) between 2000 and 2019 from recently published dataset and outlines of the Caucasus glacier inventory. We used a debris-covered glacier dataset to compare the changes between debris-free and debris-covered glaciers. We also used 30 m resolution ASTER GDEM (2011) to determine topographic details, such as aspect, slope, and elevation distribution of glaciers. Results indicate that the mean rate of glacier mass loss has accelerated from 0.42 ± 0.61 m of water equivalent per year (m w.e. a−1) over 2000–2010, to 0.64 ± 0.66 m w.e. a−1 over 2010–2019. This was 0.53 ± 0.38 m w.e. a−1 in 2000–2019. Mass loss rates differ between the western, central, and eastern Greater Caucasus, indicating the highest mean annual mass loss in the western section (0.65 ± 0.43 m w.e. a−1) in 2000–2019 and much lower in the central (0.48 ± 0.35 m w.e. a−1) and eastern (0.38 ± 0.37 m w.e. a−1) sections. No difference was found between the northern and southern slopes over the last twenty years corresponding 0.53 ± 0.38 m w.e. a−1. The observed decrease in mean annual geodetic mass balance is higher on debris-covered glaciers (0.66 ± 0.17 m w.e. a−1) than those on debris-free glaciers (0.49 ± 0.15 m w.e. a−1) between 2000 and 2019. Thickness change values in 2010–2019 were 1.5 times more negative (0.75 ± 0.70 m a−1) than those in 2000–2010 (0.50 ± 0.67 m a−1) in the entire region, suggesting an acceleration of ice thinning starting in 2010. A significant positive trend of May-September air temperatures at two selected meteorological stations (Terskol and Mestia) along with a negative trend of October-April precipitation might be responsible for the negative mass balances and thinning for all Caucasus glaciers over the study period. These results provide insight into the change processes of regional glaciers, which is key information to improve glaciological and hydrological projections in the Caucasus region. [ABSTRACT FROM AUTHOR]

The Chinese Tianshan mountainous region (CTMR) is a typical alpine region with high topographic heterogeneity, characterized by a large altitude span, complex topography, and diverse landscapes. A significant increase in air temperature had occurred in the CTMR during the last five decades. However, the detailed, comprehensive, and systematical characteristics of climate warming, such as its temporal and spatial heterogeneity, remain unclear. In this study, the temporal and spatial heterogeneity of climate warming across the CTMR had been comprehensively analyzed based on the 10-day air temperature data gathered during 1961–2020 from 26 meteorological stations. The results revealed local cooling in the context of general warming in the CTMR. The amplitude of variation (AV) varied from −0.57 to 3.64 °C, with the average value of 1.19 °C during the last six decades. The lapse rates of the elevation-dependent warming that existed annually, and in spring, summer, and autumn are −0.5 °C/100 m, −0.5 °C/100 m, −0.7 °C/100 m, and −0.4 °C/100 m, respectively. The warming in the CTMR is characteristic of high temporal heterogeneity, as represented by the amplified warming at 10-d scale for more than half a year, and the values of AV were higher than 1.09 °C of the global warming during 2011–2020 (GWV2011–2020). Meanwhile, the amplitudes of warming differed greatly on a seasonal scale, with the rates in spring, autumn, and winter higher than that in summer. The large spatial heterogeneity of climate warming also occurred across the CTMR. The warming pole existed in the warm part, the Turpan-Hami basin (below 1000 m asl) where the air temperature itself was high. That is, the warm places were warmer across the CTMR. The cooling pole was also found in the Kuqa region (about 1000 m asl). This study could greatly improve the understanding of the spatio-temporal dynamics, patterns, and regional heterogeneity of climate warming across the CTMR and even northwest China. [ABSTRACT FROM AUTHOR]

Atmospheric drought is an extremely important issue on a global, regional and local scale, especially in the context of climate change. The aim of the study was to assess the spatiotemporal variation of atmospheric (meteorological) drought in agricultural areas of east-central Poland, represented by the Lublin Voivodeship (Lublin region) in 1971–2015. Average monthly air temperatures and monthly precipitation totals recorded over the 45-year period at 25 weather stations were used in the study. The assessment of spatiotemporal variation in atmospheric drought in the study area was based on calculations of the aridity index. The analysis showed an increase in the severity of atmospheric drought in the Lublin region, with intensification of this phenomenon in the last two decades, especially in the warmer half of the year (April, June–August). The main cause of drought in the Lublin region was identified as a statistically significant increase in air temperature (on average, from 0.4 °C to 0.7 °C/10 years in April, July and August, and from 0.2 °C to 0.5 °C/10 years in June) together with the absence of changes in precipitation in the warmer half of the year. This may be linked to some increase in the frequency of high-pressure circulation types, both non-directional and advection from the south. Due to the worsening problem of drought in Poland in recent years, especially in agriculture, there is a need for further research on this subject and for solutions aimed at optimizing agricultural use of the productive environment. [ABSTRACT FROM AUTHOR]

This study was designed to better understand vegetation's impact on air maximum (Tmax), minimum (Tmin), and daily temperature range (DTR), as well as seasonality and variability. We selected a flat, under synoptic-scale, northern Serbian region with an operational network of automated weather stations (AWS) for the study. Data were collected directly from the eighteen AWSs placed in the orchard canopy during 2013–2018. Meteorological data, plant phenological data in the form of the BBCH scale, and orchards' soil characteristics data were collected. Environmental factors influencing the temperature were classified as static (slow or unchangeable) and dynamic (fast-changing). The impact of both factors on maximum, minimum, and daily temperature range and its variability were analyzed. Results show that static factors (like soil texture) affect the annual variation of Tmax, Tmin, and DTR rather than its variability over the season. The dynamic factors, mainly coming from the plant's phenology, substantially affected the seasonal variability of these variables. Studies like this suffer from missing data and sparse spatial coverage by the AWS network. Therefore, the alternatives of orchard micrometeorological data, nearest climatological station, and ERA5-Land reanalysis data are tested. Both data sets showcased limitations in their applicability, while reanalysis data deviated more from the in-situ measurements, both seasonally and regionally. [ABSTRACT FROM AUTHOR]

The characteristics of near surface air temperature and wind field over the Southeastern Levantine (SEL) sub-basin during the period 1979–2018 were simulated. The simulation was carried out using a dynamical downscaling approach, which requires running a regional climate model system (RegCM-SVN6994) on the study domain, using lower-resolution climate data (i.e., the fifth generation of ECMWF atmospheric reanalysis of the global climate ERA5 datasets) as boundary conditions. The quality of the RegCM-SVN simulation was first verified by comparing its simulations with ERA5 for the studied region from 1979 to 2018, and then with the available five WMO weather stations from 2007 to 2018. The dynamical downscaling results proved that RegCM-SVN in its current configuration successfully simulated the observed surface air temperature and wind field. Moreover, RegCM-SVN was proved to provide similar or even better accuracy (during extreme events) than ERA5 in simulating both surface air temperature and wind speed. The simulated annual mean T2m by RegCM-SVN (from 1979 to 2018) was 20.9 °C, with a positive warming trend of 0.44 °C/decade over the study area. Moreover, the annual mean wind speed by RegCM-SVN was 4.17 m/s, demonstrating an annual negative trend of wind speed over 92% of the study area. Surface air temperatures over SEL mostly occurred within the range of 4–31 °C; however, surface wind speed rarely exceeded 10 m/s. During the study period, the seasonal features of T2m showed a general warming trend along the four seasons and showed a wind speed decreasing trend during spring and summer. The results of the RegCM-SVN simulation constitute useful information that could be utilized to fully describe the study area in terms of other atmospheric parameters. [ABSTRACT FROM AUTHOR]

In this study, an improved method named spatial disaggregation and detrended bias correction (SDDBC) based on spatial disaggregation and bias correction (SDBC) combined with trend correction was proposed. Using data from meteorological stations over China from 1991 to 2020 and the seasonal hindcast data from the Beijing Climate Center Climate System Model (BCC_CSM1.1 (m)), the performances of the model, SDBC, and SDDBC in spring temperature forecasts were evaluated. The results showed that the observed spring temperature exhibits a significant increasing trend in most of China, but the warming trend simulated by the model was obviously smaller. SDBC performed poorly in temperature trend correction. With SDDBC, the model's deviation in temperature trend was corrected, and consequently, the temporal correlation between the model's simulation and the observation as well as the forecasting skill on the phase of temperature were improved, thus improving the MSSS and the ACC. From the perspective of probabilistic prediction, the relative operating characteristic skill score (ROCSS) and the Brier skill score (BSS) of the SDDBC for three categorical forecasts were higher than those of the model and SDBC. The SDDBC's BSS increased as the effect of the increasing resolution component was greater than that of the decreasing reliability component. Therefore, it is necessary to correct the predicted temperature trend in post-processing for the output of numerical prediction models. [ABSTRACT FROM AUTHOR]

The article discusses the features of large-scale spatial and temporal variability of moistening (potential evapotranspiration, precipitation, potential evapotranspiration coefficient) in the Siberian part of Russia for the period 1981–2015. The All-Russian Research Institute of Hydrometeorological Information—World Data Center (RIHMI-WDC) archive has served as a source of initial information. Due to the rare network of stationary meteorological stations in most of Siberia, only 32 stations located mainly in the valleys of large rivers have been used for calculations. To estimate potential evapotranspiration, the modified method of M.I. Budyko has been used. A comprehensive delimitation of Siberia has been carried out by the interannual fluctuations of characteristics of moistening, being well divided into four regions, three of which encompass the basins of the largest rivers: the Ob, the Yenisei, the Lena and the fourth region represents the Baikal region. Analysis of the trends shows that the evapotranspiration in Siberia is growing only in the Ob basin and the Baikal region. Precipitation, excluding the Baikal region, is also increasing in the Yenisei and Lena basins. As for the potential evapotranspiration coefficient, a significant trend refers only to the Baikal region due to the rapid increase in evaporation. The modeling of the annual values of the characteristics of moistening for the selected regions has been carried out using the decision trees method. For 4-branch trees, the coefficient of determination R2 describes about two-thirds of the variance of the original variable (0.57–0.73). In the models of annual evapotranspiration values, the main predictor is the air temperature. In precipitation models, the contribution of local and external circulation factors to interannual precipitation fluctuations is equal. [ABSTRACT FROM AUTHOR]