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Diurnal temperature range (DTR) which reflects the difference between the daily maximum (Tmax) and minimum temperature (Tmin) is an important indication of changing climate and a critical thermal metric to assess the impact on agriculture, biodiversity, water resources, and human health. The major aim of this study is to assess the probable future spatio-temporal changes in the Tmax, Tmin, and DTR and their long-term warming trend from 2006 to 2099 under two representative concentration pathways (hereafter RCP4.5 and RCP8.5) over diverse agroclimatic regions of India. The observed data from India Meteorological Department (IMD) was used to evaluate the performance of climate models (1970–2005). The result shows a very slight underestimation in DTR by models compared to the observed. In future projections, we found a reduction in DTR (0.001 to 0.020 °C/year) partly linked to the substantial increase in Tmin (0.020 to 0.071 °C/year) than Tmax (0.031 to 0.060 °C/year) that was stronger in far twenty-first-century future under RCP8.5. The decline in DTR was profound and consistent over northern India (up to 3 °C) surrounding the Indo-Gangetic Plain, western dry region, and part of central India with the highest decline observed in winter and pre-monsoon season. However, a decline in DTR was also anticipated over the plateau, coastal, and eastern Himalayas region. Change in land use land cover (LULC) also complimented the decline in DTR. The main findings of the study advocate implementation of a robust framework for climate change adaptation strategies to mitigate adverse consequences to the natural ecosystem and human health over specific regions arising due to declining DTR. [ABSTRACT FROM AUTHOR]

The Indo‐Gangetic Plain (IGP) bears great agricultural importance and contributes to a major share of national GDP of India. In present study, a location‐specific comprehensive analysis of rainfall extremes over IGP, using second generation CORDEX‐CORE simulations in the present and future scenarios (under high emission RCP8.5 scenario) have been performed. Here, the high‐resolution CORDEX‐CORE simulations with International Centre for Theoretical Physics's regional climate model (RegCM4.7) have been considered for the detailed rainfall characteristics assessment. Twelve thresholds‐based climate indices have been analyzed to investigate the characteristics of rainfall extremes during three‐time slices: 1986–2005 (historical), 2041–2060 (near future) and 2080–2099 (far future). The RegCM4 projections suggested a substantial decline in mean Indian Summer monsoon rainfall (ISMR) and wet days (rainfall ≥ 1 mm; 7%–14%) over IGP under high‐emission RCP8.5 scenario. The contribution of 90th and 99th percentile days and total rainfall on wet days, seems to be get enhanced in future by 14%–35%, which implies the increase and intensification in rainfall extremes over IGP by the end of the 21st century. Further, the decline in ISMR and negligible changes in annual rainfall over IGP suggest the possible shift of monsoon regime during the later months of the year in warming climate. Thus, findings of present study may play a crucial role in predicting the ISMR and rainfall extremes over the IGP. Therefore, it can be useful for scientists and policymakers to plan and implement mitigation strategies. Plain Language Summary: In present study, an endeavor has been made to access the Indian Summer monsoon rainfall (ISMR) patterns and monsoon extremes over Gangetic plain which is fluvially fertile land and feeds around 40% Indian population. The International Centre for Theoretical Physics' regional climate model (RegCM4.7) robustly performs with various forcings in simulating mean precipitation and 12 different threshold‐based climate indices over Indo‐Gangetic Plain (IGP) during the historical period. However, the statistical analysis suggests RegCM4 with forcings EIN75 and MPI‐ESM‐MR followed by MIROC5 and Nor‐ESM performs robustly in simulating the rainfall patterns over the IGP. Further, various forcing combinations have produced some rainfall indices with slight differences. Therefore, a ranking‐based framework has been proposed in order to choose best forcing‐combination for future projections. A closure encounter revealed that substantial decline in mean ISMR while negligible changes in annual rainfall have been projected which resembles to the fact of shift in monsoon regime toward the later months of the year under warming climate. As far as monsoon extreme are concerned, a rise in heavy rainfall days and their intensity have been projected. Also, maximum enhancement in projected heavy rainfall characteristics have been found to be near Himalayan foothills which is may be due the increased aerosol accumulation over IGP. Key Points: The RegCM4 performs satisfactorily in simulating mean and extreme monsoonal rainfall characteristics over Gangetic PlainsProjected decline in mean monsoon rainfall whereas a rise in extreme rainfall indices over the Gangetic plain under a warming climateProjected increase in extreme rainfall indices over upper Indo‐Gangetic Plain and Himalayan foothills during future time slices under RCP8.5 scenario [ABSTRACT FROM AUTHOR]

Drought frequency and intensity have increased in recent decades and are expected to escalate in future under the changing climate scenario. However, a wide range of uncertainty exists regarding the risk, variability and severity of the drought. This study evaluates the future drought and associated uncertainty over homogeneous regions of India using the suites of CMIP5 global climate models (GCMs) and CORDEX‐SA regional climate models (RCMs). The drought characteristics and its projected future changes are analysed using probability density functions derived from hydroclimatic parameters, including the standardized precipitation index (SPI) and the standardized precipitation‐evapotranspiration index (SPEI). Besides, uncertainties from various sources such as inter‐model variability, indices type, timescale, adapted methods, and time‐slices are explored under the RCP8.5 emission scenario to the end of the 21st century. Our study reveals large biases in the individual model; however, both the multi model ensembles (GCM and RCM) generally demonstrate better performance with respect to observation. In particular, the RCM ensemble showed limitations in capturing the regional precipitation pattern while temperature and potential evapotranspiration (PET) showed considerable enhancement concerning GCMs. SPI (SPEI) generally exhibited enhanced wetness (dryness) derived from increased precipitation (PET), although a few discrepancies were noticed. The regional heterogeneity was also found to exist, although some robust changes were noticed in drought frequency and severity with different return periods. Our finding underscores a wide range of uncertainties in drought projection, with maximum contribution from indices selection followed by model variability whereas other sources have the least contribution. The primary drivers for all these uncertainty sources arise due to variations among models simulated hydroclimatic variables that need to be parameterized more precisely for sustainable drought management. [ABSTRACT FROM AUTHOR]

An effort is made to identify a suitable convective parameterization scheme (CPS) in a regional climate model (RegCM4.5) for simulating the spatiotemporal variability of precipitation extremes over India. In this regard, a set of seven sensitivity experiments are conducted using different CPS. The performance of the mixed scheme, MIT over land and Grell over ocean (MLGO), bears a considerable resemblance to observations in the spatial distribution of mean extreme precipitation compared to other schemes. All schemes underestimate the mean precipitation due to heavy and extreme events, particularly over central India. However, this underestimation is considerably reduced in MLGO. The overestimation of the rainy days and underestimation of the dry days is one of the possible regions of higher mean rainfall in MLGO. Despite having considerable performance in simulating mean rainfall, MLGO fails to correctly simulate rainy days, dry days, and the frequency of events for very low intensities. However, Grell simulates the intensity and frequency of events closer to observation. In general, the Grell has the highest performance in simulating extreme events, dry/wet days, and daily precipitation intensity. The better performance of Grell and MLGO in simulating the precipitation extremes is due to the stronger south-westerly wind from the Arabian Sea (AS), and convergent flow from the northern Bay of Bengal (BoB) to central India (CI) facilitates the moisture pulling mechanisms enhancing moisture availability and hence precipitation. In addition, the higher atmospheric instability in MLGO enhances the moist convection, strengthening convective activity, leading to enhanced precipitation. [ABSTRACT FROM AUTHOR]

This study aims to project and characterize the climate extremes in Ravi River Basin (RRB) which is considered as a data poor transboundary basin located in India and Pakistan. Performance of the three GCDs against observation data was evaluated at three stations. A quantile mapping technique was used to correct the biases of four regional climate models (RCMs) and climate extremes were analysed for future period (2020–2095). Seven temperature and rainfall based indices that represent warm and wet characteristics of climate were chosen. Four statistical parameters and spatial maps were evaluated for the base period 1982–2005. The CPC‐NOAA and PU had the best performance for temperature and rainfall data regarding the time series analysis. The quantile mapping improved three important aspects of the climate cycle; the transitions from dry to wet and wet to dry seasons and peaks as well. At spatial scale, quantile mapping well captured the spatial distribution of the eleven indices other than RX1Day, CWD and FD0. The results show that warm and wet extremes will increase in future at 5% significance level across the entire basin with large changes in north east. The changes will be large for RCP8.5 scenario compare to RCP4.5 scenario and choice of the scenarios has dominant contribution in uncertainty than choice of the models. [ABSTRACT FROM AUTHOR]

Extreme temperature events derived from the global climate models (GCMs) are used for climate change impact studies in several sectors, especially in agriculture. Reducing the uncertainty in simulated crop yields based on extreme temperatures is an important task for reliable and relevant adaptation decisions. This study compared the performance of selected six global climate models in simulating temperature extreme events over Indian region for the 1976–2005 period. For this, performance statistics such as root mean square error, correlation coefficient, and agreement index were compared spatially and spatiotemporally. The study reveals that all the six models overestimate minimum and maximum temperature extremes for most parts of Central India, which resulted in hot bias. However, these models show a cold bias in simulating low temperature extremes over the Himalayan region. Further the study indicates that GFDL-ESM2M and MIROC-ESM-CHEM have performed relatively better in capturing temperature extreme events over Indian region among the six models that were found to closely simulate the observed climatology. [ABSTRACT FROM AUTHOR]

Present study attempts to project extreme precipitation indices over 34 different meteorological subdivisions and six homogeneous regions such as Northwest, Central Northeast, Northeast, West Central, Peninsular India and Hilly Region during summer monsoon season in the twenty-first century. For this purpose, the Regional Climate Model version4 (RegCM4) had been run at 50 km horizontal resolution forced with the global model GFDL-ESM2 M, during reference period 1976–2005 for the model validation, and the mid- (2031–2060) and far-future (2070–2099) for projections under RCP8.5 scenario over the South Asia CORDEX domain. In this paper, model simulated precipitation has been validated against IMD, APHRODITE and NCEP/NCAR data sets. The results indicate that RegCM4 captures the important features of seasonal precipitation and various extreme indices over the study area. The RegCM4 has projected an increase in the mean seasonal precipitation by 0.56 mm/day whereas in case of GFDL model the rate is 0.39 mm/day during the far-future relative to the reference period. The heavy precipitation indices are projected to increase more frequently (0.264/decade) than the mean precipitation rate (0.01/decade) over India. The correlations between the extreme precipitation indices and the seasonal mean precipitation are found to be strong. In addition, the consecutive dry days are projected to occur more frequently (3–5 days) over West Central (Telangana, Vidarbha and Marathwada) and west Rajasthan while consecutive wet days are projected to decrease over larger parts of India during far-future. Similarly, 1 day maximum precipitation and the simple daily intensity index are projected to increase consistently from mid- to far- futures over some sub-divisions of West coast, Hilly and Northeast regions. From a spatial probability perspective, model projection indicates more frequent severe drought and flood conditions over India. [ABSTRACT FROM AUTHOR]

Climate scenarios derived from the global climate models (GCMs) are used for climate change impact studies in several sectors including agriculture, hydrological, and health. Globally, more than 50 climate models exist and choosing suitable models based on reproducibility of observed weather for a study region is a challenging task. This step is important to reduce uncertainty. This study compared the simulation performance of 12 global climate models for temperatures and rainfall in past 30 years over Indian region. For this, Priority index from Fuzzy Analytic Hierarchy Process (FAHP) and Reliability index were used and both methods were compared. Study revealed all 12 models overestimated minimum and maximum temperatures in most regions of India, which resulted in hot bias especially in northern region. However, models showed significant cold bias for the Himalayan region. In general, simulated rainfall was underestimated by many GCMs. The analysis indicated that FAHP method is good to shortlist GCMs based on their spatial and temporal performance in reproducing observed weather. Among 12 models, NORESM1 model has performed better in reproducing maximum temperature. The IPSL-LR, FIO-ESM, GFDL-CM3, and MIROC5 models have performed better for minimum temperature. In case of rainfall, CSIRO, MIROC5, HADGEM2, GFDL-ESM 2 M, and IPSL-LR have performed better as compared to other models. [ABSTRACT FROM AUTHOR]

This study is aimed at examining the consistency of the relationship between load of dust and rainfall simulated by different climate models and its implication for the Indian summer monsoon system. Monthly mean outputs of 12 climate models, obtained from the archive of the Coupled Model Intercomparison Project phase 5 (CMIP5) for the period 1951-2004, are analyzed to investigate the relationship between dust and rainfall. Comparative analysis of the model simulated precipitation with the India Meteorological Department (IMD) gridded rainfall, CRU TS3.21 and GPCP version 2.2 data sets show significant differences between the spatial patterns of JJAS rainfall as well as annual cycle of rainfall simulated by various models and observations. Similarly, significant inter-model differences are also noted in the simulation of load of dust, nevertheless it is further noted that most of the CMIP5 models are able to capture the major dust sources across the study region. Although the scatter plot analysis and the lead-lag pattern correlation between the dust load and the rainfall show strong relationship between the dust load over distant sources and the rainfall in the South Asian region in individual models, the temporal scale of this association indicates large differences amongst the models. Our results caution that it would be pre-mature to draw any robust conclusions on the time scale of the relationship between dust and the rainfall in the South Asian region based on either CMIP5 results or limited number of previous studies. Hence, we would like to emphasize upon the fact that any conclusions drawn on the relationship between the dust load and the South Asian rainfall using model simulation is highly dependent on the degree of complexity incorporated in those models such as the representation of aerosol life cycle, their interaction with clouds, precipitation and other components of the climate system. [ABSTRACT FROM AUTHOR]