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Temperature rise is a concern for future agriculture in different regions of the globe. This study aimed to reveal the future changes and variabilities in minimum temperature (Tmin) and maximum temperature (Tmax) in the monthly, seasonal, and annual scale over Bangladesh using 40 General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 5 (CMIP5) for two radiative concentration pathways (RCPs, RCP4.5 and RCP8.5). The statistical downscaling climate model (SimCLIM) was used for downscaling and to ensemble temperature projections (Tmax and Tmin) for the near (2021–2060) and far (2071–2100) periods compared to the base period (1986–2005). Multi-model ensemble (MME) exhibited increasing Tmax and Tmin for all the timescales for all future periods and RCPs. Sen’s slope (SS) analysis showed the highest increase in Tmax and Tmin in February and relatively less increase in July and August. The mean annual Tmax over Bangladesh would increase by 0.61°C and 1.75°C in the near future and 0.91°C and 3.85°C in the far future, while the mean annual Tmin would rise by 0.65°C and 1.85°C in the near future and 0.96°C and 4.07°C in the far future, for RCP4.5 and RCP8.5, respectively. The northern and northwestern parts of the country would experience the highest rise in Tmax and Tmin, which have traditionally been exposed to temperature extremes. In contrast, the southeastern coastal region would experience the least rise in temperature. A higher increase in Tmin than Tmax was detected for all timescales, signifying a future decrease in the diurnal temperature range (DTR). The highest increase in Tmax and Tmin will be in winter compared to other seasons for both the periods and RCPs. The spatial variability of Tmax and Tmin changes can be useful for the long-term planning of the country.

The changes in temperature regime on the territory of Russia and adjacent areas are evaluated using the 20-year archive data of the Hydrometeorological Research Center of the Russian Federation. The dynamics was evaluated for the average annual temperature, the amplitude and phase of its seasonal fluctuations, the dates of the last and first freeze. The results significantly depend on geography and do not give grounds for the conclusion that the nature of the changes is anthropogenic.

The climate change assumes nowadays on significance. Weather data may be available on various time scales – long-term, minutes, hours, days, periods, years. Measurements of air temperature are realized for a long time. Data in Slovakia are available from few weather stations of Slovak Hydrometeorological Institute (SHMI). For long-term and wide-ranging measurement of various parameters this can be complicated and expensive. This paper is focused on temperature measurement near the experimental laboratory. Estimation of daily, monthly and yearly mean temperatures is done in different ways. Results from experimental temperature measurement, in a way of selection of unusual extremes are presented. Shorter recording intervals describe the temperature courses in a more pertinent way.

The annual and seasonal (kharif and rabi seasons) trends in temperature (maximum and minimum) and rainfall during 30 years (1986 to 2015) at five locations of Punjab (Bathinda, Faridkot, Ludhiana, SBS Nagar and Patiala) has been analysed. The study revealed that during the last three decades most of the stations experienced significant increase in maximum as well as minimum temperatures in both kharif and rabi seasons. In kharif season maximum temperature positively deviated by 0.12°C to 1.34°C whereas in wheat growing period the deviation results was from 0.13°C to 0.93°C at all the locations. The rainfall during kharif season has decreased by 23.2, 27.6, 55.4, 185.4 and 199.6 mm from normal at Bathinda, Faridkot, Ludhiana, SBS Nagar and Patiala districts, respectively in lastthree decades. Mann Kendall statistics showed increasing trend in maximum temperature and highlighted that the trend was highly significant in Faridkot, SBS Nagar and Patiala district. To know the impact of climate change quadratic form of regression was applied and the results revealed that the increase in minimum temperature during kharif and rabi season has negative effect on yield of rice and wheat crop in Punjab.

The variability of minimum and maximum temperature and the daily temperature range (DTR) in Poland was analyzed on the basis of the data from 9 stations with different periods of data (the longest was 98 yr). The long-term changes of seasonal means as well as for all Julian days were determined. The increase in the minimum temperature was accompanied by a slighter increase in the maximum temperature and a decrease in the DTR. It was found that the DTR changes correlate well with cloudiness, and the extreme temperature changes are related to the NAO (North Atlantic Oscillation) intensity, especially during winter and spring. The analysis of intra-annual changes has shown that the strongest increase in the minimum and maximum temperatures occurs in mid- and late winter, but there are also periods with decreasing tendencies, i.e. late autumn, the beginning of winter and the beginning of summer. All temperature indices indicate the cooling in autumn.

We analyzed the annual, monthly, and seasonal variations of urban heat island (UHI) intensity in Istanbul by using meteorological data measured for the period of 1960–2012 at six stations. The UHI on minimum temperature is found to be positive for all seasons, and the average UHI intensity clearly indicates seasonal changes, strongest in summer and weakest in winter. The results demonstrated increase of night time UHI intensity with 0.41–0.50 °C/decade and decrease of daytime UHI intensity with 0.13–0.18 °C/decade at the urban sites. The UHI strengthened with the expansion of the city due to increased population. The influences of meteorological variables on seasonality of the UHI intensity are examined for the days categorized depending on wind, cloud cover, and precipitation values. It is found that the UHI intensity decreases with increasing wind speed and cloud cover. The integrated response of the city atmosphere to wind speed changes differ such that daytime UHI in urban atmosphere intensifies rapidly from calm conditions to the wind speeds of 2–3 m/s, then slightly increases until 4–5-m/s wind speeds and starts to decline afterwards. On the other hand, the nighttime UHI intensities in urban sites continuously decline with the same rate until the wind speeds reach to 5–6 m/s. The difference of daytime UHI between rainy summer days and dry days is around 1 °C which is almost independent of the precipitation amount. Both nighttime and daytime UHI intensities depend on the season and site range approximately between 0.24 and 1.74 °C and − 0.62 and 2.61 °C, respectively. However, the UHI based on minimum temperature for the selected dry days with low wind and clear sky conditions increases to 1.70–3.08 °C. Land surface data from Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra show areal extension of the UHI through the north along the Bosphorus between 2000 and 2012, especially in the night observations. The continuous increase of built-up areas, paved roads, and decrease of green areas caused the growth of UHI intensity. The estimated UHI based on land surface temperature (LST) at the most urbanized locations of Istanbul reach to 8 °C for daytime and 6 °C for nighttime.

The majority of people in South Africa eat maize, which is grown as a rain-fed crop in the summer rainfall areas of the country, as their staple food. The country is usually food secure except in drought years, which are expected to increase in severity and frequency. This study investigated the impacts of rainfall and minimum and maximum temperatures on maize yield in the Setsoto municipality of the Free State province of South Africa from 1985 to 2016. The variation of the agroclimatic variables, including the Palmer stress diversity index (PSDI), was investigated over the growing period (Oct–Apr) which varied across the four target stations (Clocolan, Senekal, Marquard and Ficksburg). The highest coefficients of variance (CV) recorded for the minimum and maximum temperatures and rainfall were 16.2%, 6.2% and 29% during the growing period. Non-parametric Mann Kendal and Sen’s slope estimator were used for the trend analysis. The result showed significant positive trends in minimum temperature across the stations except for Clocolan where a negative trend of 0.2 to 0.12 °C year−1 was observed. The maximum temperature increased significantly across all the stations by 0.04–0.05 °C year−1 during the growing period. The temperature effects were most noticeable in the months of November and February when leaf initiation and kernel filling occur, respectively. The changes in rainfall were significant only in Ficksburg in the month of January with a value of 2.34 mm year−1. Nevertheless, the rainfall showed a strong positive correlation with yield (r 0.46, p = < 0.05). The overall variation in maize production is explained by the contribution of the agroclimatic parameters; the minimum temperature (R2 0.13–0.152), maximum temperature (R2 0.214–0.432) and rainfall (R2 0.17–0.473) for the growing period across the stations during the study period. The PSDI showed dry years and wet years but with most of the years recording close to normal rainfall. An increase in both the minimum and maximum temperatures over time will have a negative impact on crop yield.

The increase in computational resources has enabled the emergence of multi-model ensembles of convection-permitting regional climate model (CPRCM) simulations at very high horizontal resolutions. An example is the CORDEX Flagship Pilot Study on “Convective phenomena at high resolution over Europe and the Mediterranean”, a set of kilometre-scale simulations over an extended Alpine domain. This first-of-its-kind multi-model ensemble, forced by the ERA-Interim reanalysis, can be considered a benchmark dataset. This study uses a recently proposed metric to determine the added value of all the available Flagship Pilot Study hindcast kilometre-scale simulations for maximum and minimum temperature. The analysis is performed using state-of-the-art gridded and station observations as ground truth. This approach directly assesses the added value between the high-resolution CPRCMs against their driving global simulations and coarser resolution RCM counterparts. Overall, models display some modest gains, but also considerable shortcomings are exhibited. In part, these deficiencies can be attributed to the assimilation of temperature observations into ERA-Interim. Although the gains for the use of kilometre-scale resolution for temperature are limited, the improvement of the spatial representation of local atmospheric circulations and land–atmosphere interactions can ultimately lead to gains, particularly in coastal areas.

The need for adequate solar radiation is ever increasing for various applications. However there is an inadequate data of solar radiation in many countries due to the cost of instrument set up. Hence this study investigates two models for estimating solar radiation from routinely measured meteorological parameters. The data were obtained from the International Institute of Tropical Agriculture, Ibadan. The regression coefficients of the quadratic models were determined and used to estimate the global solar radiation for both forward and backward predictions. Their predictive accuracies were compared with four other models and the measured values using standard statistical error indicators. The results showed for forward as compared to backward predictions in bracket root mean square errors 1.2 (1.1); mean bias errors 1.1 (0.8) and mean percentage errors -4.8% (-2.9%) while for backward prediction 1.9 (1.7), 1.7 (1.4) and 7.9% (2.2%) measured in KJm day respectively. A positive error value shows an over estimation while a negative value shows an under estimation. The models are versatile for estimating global solar radiation at the horizontal surface, fixing missing data and correcting outliers.

Temperature is a transcendental variable in aspects such as evapotranspiration calculation, growth, development and yield of plants, in the study of the transmission pests and diseases, in the weather forecast, in determination of heat fluxes, in the calculation of the real vapor pressure, all these processes affected by global warming. The objective of this work was to compare the best results of two models: one of artificial neural network (RNA) backpropagation, and another of Fourier series. Daily data of maximum temperatures (Tmax) and minimum (Tmin) of the Santa Rosa 1, Ruíz Cortínez, Batequis and Santa Rosa 2 stations, of the Irrigation District 075 Valle del Fuerte, Los Mochis, Sinaloa, Mexico were used. In RNA, 1484 data vectors were used for training, validation and testing and 229 to forecasting. For training, the input variables of the RNA were: Julian day, longitude, latitude and altitude. Were obtained 96 scenarios with one, two and three hidden layers, with different numbers of neurons in each hidden layer. With the 1484 data, the best adjustments were obtained for the Fourier series models for maximum and minimum temperatures, and 229 data were predicted for the four stations. The best RNA backpropagation models for the prediction of maximum and minimum daily temperatures obtained similar performances in comparison with those made by the best models of Fourier series, for the study stations.