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The applicability and accuracy of high‐resolution Real‐Time Meso‐scale Analysis (RTMA) system is assessed over India for the first time. The RTMA is a high‐spatial (2.5 km) and temporal resolution analysis system for near‐surface weather conditions. It is used to simulate near‐surface air temperature over India during the Heatwave (HW) period 12th to 20th April 2023. The verification analysis of temperature using the GLDAS gridded temperature shows reasonable improvement in the analysis from RTMA by capturing regional features compared to first‐guess. The spatial and temporal verification using the IMD station observations also confirms the value addition of RTMA in capturing the locations of recorded highest Tmax and their daily variations. The location‐specific heat stress analysis of RTMA shows high skill over many locations during HW days. Heat stress regions have been accurately brought out in the RTMA. Hence, the RTMA can be used for now‐casting and severe weather monitoring. Plain Language Summary Heat wave (HW) alert systems require accurate high‐resolution surface weather data for better adaption strategies. Real‐Time Meso‐scale Analysis (RTMA) system configured to generate surface weather parameters at a high‐spatial (2.5 km) and temporal (3 hrs) resolution at National Center for Medium Range Weather Forecasting (NCMRWF) for the very first time in India. We verified its performance in representing the HW conditions and extreme temperatures over the Indian region relative to India Meteorological Department (IMD) observations/Global Land Data Assimilation System (GLDAS) during the HW period of 12th to 20th April 2023. Our results indicate significant improvement in the spatial and temporal variations of daily maximum temperature simulated by RTMA system. We have noted lower absolute mean errors of ∼ 0.02 for the RTMA in predicting the Tmax over the HW affected regions of Bihar, Odisha, West Bengal, and East Uttar Pradesh when compared to the GFS model. These improvements have led to the better identification of the locations of higher heat risk through the computation of the Heat Index and highlight the applicability of RTMA. Overall this study clearly brings out the usability of RTMA final analysis fields for now‐casting and severe weather monitoring. Key Points It is a first‐of‐its‐kind study to understand the applicability and accuracy of high‐resolution RTMA over India by analyzing heatwave RTMA temperature analysis shows higher skill over the heatwave regions and locations as compared to the background fields and observations The study suggested that the RTMA final analysis fields are highly useable for severe weather monitoring and impact‐based weather forecasts

A study was conducted with an objective to analyze how often high temperature events occur during the reproductive phases of wheat (January-March) in Punjab. Historical temperature data was analyzed to understand the probability of occurrence of temperature higher than the mean and its different combinations (mean+0.5SD, mean+1.0SD, mean+1.5SD and mean+2SD) during different standard meteorological weeks (SMW). It was found that at Ludhiana (central Punjab) the highest probability of maximum temperature (Tmax) and minimum temperature (Tmin) being higher than range was 16.9% and 18.8% during 9th and 4th SMW, respectively. At Ballowal Saunkhri (northeastern Punjab) the maximum probability of occurrence of Tmax higher than range was 16.4% during 6th and 12th SMW, respectively and that for Tmin higher than range was 20% during 9th SMW. At Bathinda (southwestern Punjab) the highest probability of occurrence of Tmax and Tmin above range was 19.7% and 19.4% during 13th and 11th SMW, respectively. In northeastern and southwestern regions of Punjab the probability of having Tmax and Tmin above range was maximum during 12-13th and 9th-11th SMW, respectively, while in central region it was maximum during 9th and 4th SMW, respectively. This implies that wheat crop should be managed adequately during these periods to avoid damage due to heat stress.

Abstract The extreme temperature events are a concern in recent years due to climate variability particularly in India as there is an increase in the temperature intensity, frequency, and duration. This study represents stationary temperature-duration-frequency (TDF) analysis over two mega cities in India Delhi (north) and Bengaluru (south) using the daily maximum temperatures at meteorological stations for the period 1969–2016 observed by India Meteorological Department (IMD).The interannual variability of maximum temperature and the maximum daily recorded value indicates the increasing trend in both the cities. The study investigates the extreme analysis of the maximum temperature using two distributions, i.e., Gumbel’s Extreme Value Type 1 (GEVT) and Log Pearson Type III (LPT), for return periods 2, 5, 10, 25, 50, and 100 years at both the locations and the positive temporal trend is observed. The TDF curves were build using annual maximum temperature values for total 8 durations (different days) of 48 years analyzed and results show the increasing trend of maximum temperature at lower duration and high return period values. The TDF is also used for prediction of the maximum temperature for the 2 hottest years in India, i.e., 2012 and 2015, and it is comparable with the observed maximum temperature. Similarly, the predictions for 11 years, i.e. 2006 to 2016, over both the cities are simulated using both the GEVT-I and LPT-III and the models have better potential skill in predicting the extreme maximum temperature. These results can be useful for the sectors like health, energy, agriculture, urban management, and ecology management and can help the policy decision makers and disaster managers in the mitigation and adoption steps to face the extreme temperature disaster at city scale.

Rising temperatures due to global warming necessitate immediate evaluation of heatwave patterns in Peninsular Malaysia (PM). For this purpose, this study utilized a locally developed heatwave index and a gridded daily maximum temperature (Tmax) dataset from ERA5 (1950–2022). During validation, the ERA5 dataset accurately represented the spatial pattern of Level 1 heatwaves, showing widespread occurrence. Historically, Level 1 heatwaves prevailed at 63.0%, followed by Level 2 at 27.7%, concentrated in northwestern states and the enclave between the Tahan and Titiwangsa mountain ranges. During very strong El Niño events in 1982/83, 1997/98, and 2015/16, Level 2 heatwave distributions were 10.4%, 26.8%, and 15.0%, respectively. For future projection, the model ensemble was created by selecting top-performing Global Climate Models (GCMs) using Kling-Gupta efficiency (KGE), ranked re-aggregation with compromise programming index (CPI), and GCM subset selection via Fisher-Jenks. The linear scaling bias-corrected GCMs (BC-GCMs), NorESM2-LM, ACCESS-CM2, MPI-ESM1-2-LR, ACCESS-ESM1-5, and FGOALS-g3, were found to exhibit better performance, and then ensemble. March to May show the highest increase in all scenarios, ranging from 3.3 °C to 4.4 °C for Level 1 heatwaves and 4.1 °C to 10.7 °C for Level 2 heatwaves. In the near future, SSP5-8.5 projects up to a 40.5% spatial increase for Level 1 heatwaves and a 2.3% increase for Level 2 heatwaves, affecting 97.1% and 57.2% of the area, respectively. In the far future, under SSP2-4.5 and SSP5-8.5, Tmax is projected to rise rapidly (1.5–4.5 °C) in the northern, western, and central regions, with increasing population exposure anticipated in the northern and western regions.

Although many studies have analyzed the effects of urbanization on temperature changes, the urbanization's effect on temperature change remains controversial. Northeast China is the largest old industrial base in China, which experienced a rapid urbanization in the past decades. Under the background of climate change, understanding the changes of surface air temperature and urbanization effects on temperature changes in the Northeast China is important to predict climate change in China. By analyzing the historical climate data, our results suggested that minimum temperature (Tmin) over the Northeast China increased significantly (0.40°C decade^(-1)) from 1960 to 1989, but showed no significant change (-0.02°C decade^(-1)) during 1990 - 2016. Due to slight change of maximum temperature (Tmax), the diurnal temperature range (DTR) showed a significant decreasing trend before 1989 (-0.34°C decade^(-1)), but reached a stable level after 1990 (-0.06°C decade^(-1)). In Northeast China, urbanization had a significant warming effect on Tmin during the night-time, but had different effects on Tmax during the daytime under the different changes of solar radiation before and after 1990. There were moderate warming effects of urbanization on Tmax during the solar dimming period of 1960 - 1989, but weak cooling effects on Tmax during the solar stable period after 1990. Due to obvious warming effect of urbanization on Tmin, urbanization in Northeast China tends to result in an increase of mean temperature but a decrease of DTR.

The aim of this study is to investigate long-term spatial changes (LTSC) of monthly maximum temperature (MMT) using NOAA-CIRES-DOE Twentieth Century Reanalysis data and different Kriging methods (KM). In this study, MMT data for 2 m above the ground during the 1836–2019 period were applied, and for spatial analysis, various KMs (ordinary, simple, and general) were used. Also, to determine the pattern of MMT distribution, the global and local Moran’s Spatial Autocorrelation Method (MSAM) was used. The results showed that the simple Kriging method with Gaussian semivariogram model has the lowest error among all methods and best explains the pattern of spatial distribution of MMT in Iran. Therefore, this method was used to map the interpolations. Interpolation maps show that the MMT distribution of Iran is a spatial function of geographical features. In the northwest of Iran and Caspian coast, it is less, and in the lowlands and plains of the south and southwest, it is more. The results of MSAM also indicate that the MMT of Iran has a cluster pattern. In the southern regions of the pattern, it is high cluster, and in the northwestern regions of the pattern, there is low cluster. According to the results, a decreasing trend of MMT and cold spots has always been observed in the northwestern regions of Iran, and an increasing trend and hot spots of MMT are observable in the southern regions. This is contrary to the results of studies conducted in Iran, which with data of up to 60 years show that the pattern of MMT distribution in all regions of Iran is increasing.

This study investigates the relation between daily solar irradiance (SI) and maximum air temperature (Tmax) over six cities in Iraq (Basra, Shanafiya, Baghdad, Rutba, Kirkuk, and Shakhan) using NASA POWER data for the period 2014-2023. Results revealed that in the arid southern provinces such as Basra, the annual mean SI exceeds 5.6 kWh m-2day-1 and Tmax frequently exceeds 48°C during the summer months while in the northern provinces lower SI and Tmax values were observed. The seasonal variation indicated peak values of SI during June while peak values of Tmax were observed in July-August. A strong relationship between SI and Tmax were obtained with R2 of 0.75 to 0.82 at different locations.

The temperature in the mountains has been increasing at an unprecedented rate in the global warming era. As a result, it is necessary to evaluate suitable models that could provide precise maximum temperature estimates. This paper explores the goodness-of-fit of the two-parameter bell-shaped, light-tailed, and heavy-tailed distribution functions for modeling the annual maximum temperature in the Northwest Himalayan region of India. The distributions under consideration are Gamma, Gumbel, Lognormal, Normal, and Weibull. Method of maximum likelihood estimation is used for parameter estimation along with Akaike information criteria for model selection. Gridded data from Climate Research Unit, UK, was obtained at the 525 grids of the region. This study shows that Normal distribution gives the best fit followed by Lognormal and Gamma distributions, and these three models jointly fit all the grids in the region. Furthermore, we estimate the 5, 10, 20, 50, 100, and 500 years return level of annual maximum temperature starting from 2017. The future projections reveal that, on average, the region will face 1.28(1.25-1.32)∘C, 1.64(1.60-1.67)∘C, 1.93(1.89-1.97)∘C, 2.26(2.22-2.31)∘C, 2.49(2.44-2.54)∘C, and 2.94(2.88-3)∘C temperature rise by the years 2022, 2027, 2037, 2067, 2117, and 2517, respectively. In comparison to the middle of the region, the higher and lower belts of the region will be severely impacted.

Quantifying the vulnerability of population to multi-faceted climate change impacts on human well-being remains an urgent task. Recently, weather and climate extremes have evolved into bivariate events that heighten climate risks in unexpected ways. To investigate the potential impacts of climate extremes, this study analyzes the frequency, magnitude, and severity of observed and future compound hot-dry extremes (CHDEs) over East Africa. The CHDE events were computed from the observed precipitation and maximum temperature data of the Climatic Research Unit gridded Timeseries version five (CRU TS4.05) and outputs of climate models of Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, this study quantifies the population exposure to CHDE events based on future population density datasets under two Shared Socioeconomic Pathways (SSPs). Using the 75th/90th and 25th/10th percentile of precipitation and temperature as threshold to define severe and moderate events, the results show that the East African region experienced multiple moderate and severe CHDE events during the last twenty years. Based on a weighted multi-model ensemble, projections indicate that under the SSP5-8.5 scenario, the frequency of moderate CHDE will double, and severe CHDE will be 1.6 times that of baseline (i.e., an increase of 60%). Strong evidence of an upward trajectory is noted after 2080 for both moderate and severe CHDE. Southern parts of Tanzania and northeastern Kenya are likely to be the most affected, with all models agreeing (signal-to-noise ratio, SNR > 1), indicating a likely higher magnitude of change during the mid- and far-future. Consequentially, population exposure to these impacts is projected to increase by up to 60% for moderate and severe CHDEs in parts of southern Tanzania. Attribution analysis highlights that climate change is the primary driver of CHDE exposure under the two emission pathways. The current study underscores the urgent need to reduce CO2 emissions to prevent exceeding global warming thresholds and to develop regional adaptation measures.

The increased temperature and humidity in the atmosphere under global warming is the primary cause of the upsurge of heat waves in the tropical belt. The central east coast of India (CECI; Odisha, Andhra Pradesh, and Telangana) is one of the most heavily affected areas in terms of casualties due to heat waves during pre-monsoon (March–May; MAM). Thus, there is a need to analyze the characteristics of pre-monsoon weekly maximum temperature (Tmax) and associated heat waves over the CECI. In the present study, characteristics of weekly Tmax from 23 March to 31 May over the CECI associated with heat waves have been analyzed using the India Meteorological Department gridded (1º × 1º) analysis data set of daily maximum temperature for the period 1980–2015. The recent changes in the weekly Tmax and frequency of various heat-wave spells (1-, 2-, 3-, and 5-day) were also evaluated. The results suggest that the climatological weekly Tmax along the coastal region is less than that in the interior parts for all 10 weeks, and the inter-annual variability and coefficient of variation exhibit similar patterns. The continuous increase in Tmax and its variability is observed as the season progresses, leading to increased intensity and frequency of heat waves in most parts of the CECI. In the recent period, a notable increase in the weekly Tmax and its variability has been observed over most parts of the CECI that has resulted in more heat waves. This study is very beneficial for determining the effects on various sectors for the planning of adaptation methodologies through appropriate strategies for a tolerable future over the CECI in the context of global warming.