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The frequency and intensity of extreme precipitation events are on the rise worldwide. Despite extensive efforts, regional climate models still show significant biases for extreme precipitation events, often due to factors like improper physics, the choice of land surface model, and spatial domain. Thus, this study uses a Coupled Land–Atmosphere Regional Climate Model version 4.7 (RegCM4.7) to explore how the choice of land surface models (LSMs) and domain extent affects the simulation of extreme precipitation over India. In this regard, a total of four sensitivity experiments have been carried out using two LSMs (CLM4.5 and BATS) over each of the two domains (one over the bigger South Asia CORDEX domain and another for the smaller domain over the Indian region). The main objective is to provide a holistic idea for obtaining an optimum model domain and LSMs for precipitation extremes over India. The model performance is demonstrated for extreme precipitation and associated processes. The result shows the systematic discrepancy in simulating extreme precipitation with a strong inter-simulation spread, indicating the strong sensitivity of extreme precipitation on the LSMs as well as the model domain. The BATS configuration shows a significant overestimation of consecutive wet days and very low precipitation, partially associated with a deficiency in convection. By contrast, the considerable underestimation of intense precipitation can be attributed to the presence of frequent, light drizzle, which hinders the accumulation of moisture in the atmosphere to a sufficient degree to prevent extreme rainfall. Despite significant improvement, the best-configured model (CLM with Indian domain) still indicates substantial bias for extreme precipitation. This deficiency in the model could potentially be mitigated by enhancing both horizontal and vertical resolutions. Nevertheless, further research is needed to explore other physics parameterizations and dynamic mechanisms to address this issue. [ABSTRACT FROM AUTHOR]

This study demonstrated the influence of downscaling using the regional climate model (RCM) driven by Era-Interim reanalysis (EIN) in simulating different aspects of the Indian summer monsoon (ISM). It is also examined, whether increasing the horizontal resolution of RCM will inevitably be capable of adding more information to ISM characteristics and its spatio-temporal variability. In this regard, two RCM (at 50 km: Reg50 and 25 km: Reg25) simulations were conducted for six years from 2000 to 2005 for the South Asia Coordinated Regional Downscaling Experiment (CORDEX) domain. The added value (AV) is found to be strongly dependent on region and considered metrics. A slight improvement towards increasing spatial resolution is observed in the simulation of the mean ISM characteristics, while considerable improvements are noticed for the frequency distribution of extremes. The notable improvement in the daily climatology of precipitation is observed over the region of northeast India (~ 35%) and the Hilly region (~ 32%) and the lowest improvement over north-central India (~ 8%). The reduction of anomalously strong northeasterly flow over the southeastern Arabian Sea and strengthening of the moisture leaden southeasterly wind flow from the Bay of Bengal in Reg25 compared to Reg50 is consistent with the reduction of dry bias over India in Reg25. The robust improvements are noticed for the heavy precipitation events (probability density function: PDF tails) and mean precipitation due to extreme precipitation events, particularly over the areas characterized by complex topographical features (e.g., the Western Ghats, Indo-Gangetic plains, and northeast India and Hilly regions) as well as over the areas having substantial bias (e.g., central India), indicating its strong sensitivity towards model resolution. The increasing latent heat flux in Reg25 contributes to increasing the moisture and hence rainfall over India. Both simulations apparently simulate many of the ISM characteristics better than the EIN, thereby emphasizing the usefulness of finer resolutions in the better simulation of the Indian monsoon, especially for heavy rainfall. However, the RegCM bias is comparable to or even greater in some places than the EIN bias. This suggests that high-resolution models are important for improving performance; however, it does not necessarily mean that they can have AV for every aspect and all places. Apart from this, the substantial difference in the AV over different regions or aspects highlights the importance of carefully selecting AV matrices for the different areas and characteristics being investigated. RegCM exhibits some systematic biases in precipitation despite substantial improvement due to misrepresentation of dynamical and thermodynamical processes, including northward and eastward propagating convective bands. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon rainfall (ISMR, June–September) is an important factor in agricultural planning and the Indian economy. The ISMR over East India (EI: Chhattisgarh-Bihar-Jharkhand-Odisha) is particularly significant, and it can have an impact on the country's average ISMR. The current study examined projected changes in ISMR over EI with two emission scenarios, RCP4.5 and RCP8.5, for near (2017–2040) and far-future (2041–2070) projections using a set of ten CORDEX-SA Regional Climate Model (RCMs). To begin, the performance of raw and bias-corrected ISMR over EI outputs from ten CORDEX-SA RCMs was compared to a high-resolution (0.25° × 0.25°) gridded rainfall analysis dataset from the India Meteorological Department (IMD) over the hindcast period (1971–2005). Following that, bias-corrected results were used to calculate ISMR projections over EI for the near and distant futures. Most RCMs, according to the findings, can imitate the spatial pattern of ISMR across EI but are restricted in their ability to capture actual magnitudes. Notably, RCM prediction skill increased greatly after employing various bias-correction approaches, the quantile mapping (QQM) bias-correction technique outperformed other current conventional bias correction methods, and the QQM technique was employed for ISMR future projections using RCP4.5 and RCP8.5 emission scenarios. The analysis of ISMR projections over EI reveals that there will be more deficit rainfall years in the short term while more excess rainfall years in the far future. [ABSTRACT FROM AUTHOR]

The Tropical Cyclonic Disturbances (TCDs) over the Bay of Bengal (BoB) have always disrupted life, economy and environment across the coastal regions. The present study evaluates the ability of COoordinated Regional Climate Downscaling Experiment (CORDEX) constituting regional climate model (here REMO2009) in simulating the behaviour of some precursors of TCDs, frequency of TCDs and their intensity over the BoB. Furthermore, the impacts of El Niño Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD) are addressed to determine the sensitivity of the model in capturing large-scale ocean-atmosphere coupled phenomena. The model outputs (resolution 0.44° × 0.44°) are evaluated against the recorded observations of India Meteorological Department and ERA-Interim reanalysis (resolution 0.25° × 0.25°) over the time period 1979–2005. Evaluation of TCD frequencies and intensities on a year-to-year basis shows the model performing reasonably well against observations but intensity is largely reduced. Also, we find an overestimated number of TCDs in the model as pre-monsoon (post-monsoon) shows + 195% (+ 80%) more TCDs. The large-scale environmental fields associated with TCDs show spatiotemporal biases of varying magnitudes in the model however are consistent in capturing TCDs and their behaviours. The mean climatology shows clear differences in environmental fields during days with TCDs and without TCDs. The genesis geolocations in observations are coherent with their environmental fields and are firmly reproduced in the model albeit with spatial differences. The intensity in the model is found to be mostly low showing weak TCDs besides overestimating (underestimating) TCDs of moderate (high) intensities. The REMO2009 model is found satisfactorily simulating the TCDs (year-to-year basis) and associated large-scale environmental fields against the observations and reanalysis. The impacts of large climatic teleconnections (ENSO and IOD) are also captured in the model with warm ENSO phase suppressing the TCDs activities while cold phase triggering the TCDs. On the similar watch, a negative dipole over the Indian Ocean triggers the TCDs while a positive dipole suppresses the TCDs formation in the model. The present study using the regional climate model (RCM) REMO2009 is moreover a needed baseline for the future projections and evaluation of other RCMs under the CORDEX domains. [ABSTRACT FROM AUTHOR]

Climate change is expected to exacerbate the hydrological cycle globally and have a significant impact on water resources. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report states that observed and projected increases in both temperature and precipitation variability are the main reasons for projected climate change impacts on natural water resources. The examination of meteorological variables of the region, especially when agriculture is rainfall dependent, is very essential to formulate feasible adaptation strategies. As a result, using CORDEX-SA (Coordinated Regional Downscaling Experiment-South Asia) rainfall data (2021 to 2050), trend analysis was used to examine variations in rainfall data in the Kokkarne catchment of the Seetha river basin. Regression analysis was used to identify the season-wise rainfall trend. Annual, Summer, Monsoon, and Winter rainfall have depicted increasing trends with a rate of 2.46, 1.21, 2.77, and 0.009 mm per year respectively. The post-monsoon rainfall has projected a declining trend with a rate of -1.54 mm per year. Hence it is recommended that the designed strategies in the agricultural sector have to take the increasing, decreasing, and erratic nature of the trend of rainfall into consideration. Further considering the use of a Multi-Model Ensemble (MME) is reducing the SD and CV of rainfall data by 862 mm and 48.5% respectively. 87% of annual rainfall is contributed by monsoon season only with a Standard deviation of 424.4 mm and CV of 12%. [ABSTRACT FROM AUTHOR]

A new high-resolution Regional Earth System Model, namely ROM, has been implemented over CORDEX-SA towards examining the impact of air–sea coupling on the Indian summer monsoon characteristics. ROM's simulated mean ISM rainfall and associated dynamical and thermodynamical processes, including the representation of northward and eastward propagating convention bands, are closer to observation than its standalone atmospheric model component (REMO), highlighting the advantage of air–sea coupling. However, the value addition of air–sea coupling varies spatially with more significant improvements over regions with large biases. Bay of Bengal and the eastern equatorial Indian Ocean are the most prominent region where the highest added value is observed with a significant reduction up to 50–500% precipitation bias. Most of the changes in precipitation over the ocean are associated with convective precipitation (CP) due to the suppression of convective activity caused by the negative feedback due to the inclusion of air–sea coupling. However, CP and large-scale precipitation (LP) improvements show east–west asymmetry over the Indian land region. The substantial LP bias reduction is noticed over the wet bias region of western central India due to its suppression, while enhanced CP over eastern central India contributed to the reduction of dry bias. An insignificant change is noticed over Tibetan Plateau, northern India, and Indo Gangetic plains. The weakening of moisture-laden low-level Somalia Jets causes the diminishing of moisture supply from the Arabian Sea (AS) towards Indian land regions resulting in suppressed precipitation, reducing wet bias, especially over western central India. The anomalous high kinetic energy over AS, wind shear, and tropospheric temperature gradient in REMO compared to observation is substantially reduced in the ROM, facilitating the favourable condition for suppressing moisture feeding and hence the wet bias over west-central India in ROM. The warmer midlatitude in ROM than REMO over eastern central India strengthens the convection, enhancing precipitation results in reducing the dry bias. Despite substantially improved ROM'performance, it still exhibits some systematic biases (wet/dry) partially associated with the persistent warm/cold SST bias and land–atmosphere interaction. [ABSTRACT FROM AUTHOR]

The study focused on the Godavari River basin to understand the alteration in the drought phenomenon for future scenarios. The Standardized Precipitation Evapotranspiration Index (SPEI)-3 is calculated from Climate Research Unit 4.03 precipitation, and minimum and maximum temperatures. The drought magnitude and characteristics are determined using SPEI, which considers both precipitation and temperature data as input variables. The Mann–Kendall trend analysis is performed to identify the trend associated with drought characteristics. The basin is divided into six homogeneous regions using K-means clustering algorithm. The reliability ensemble averaging method is used for ensemble averaging of regional climate models (RCMs). The drought frequency analysis is carried out using trivariate copula for reference and future time periods. Variations in the drought characteristics are observed in the future scenarios with respect to the reference period. The drought duration, severity and peak for different climate divisions showed an increasing trend in future time period, especially in the case of RCP8.5 scenarios. The return periods of future droughts based on weighted-average RCMs under the two scenarios showed the possibility of more frequent droughts in the future (2053–2099) than in the past (1971–2017). HIGHLIGHTS The Standardized Precipitation Evapotranspiration Index (SPEI) for historical and future periods is computed.; The SPEI is used to assess drought characteristics in the Godavari River basin.; The river basin is divided into six regions using K-means clustering algorithm.; The trend analysis is carried out using historical and future drought characteristics.; Future drought return periods are computed using trivariate copula.;

Present study is an attempt to evaluate the rainfall pattern obtained from the selected Regional Climate Models (RCMs) which participated in Coordinated Regional Climate Downscaling Experiment for the South Asia region (CORDEX‐SA) and the Global Climate Models (GCMs) which participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) with respect to the high‐resolution ground‐based Indian Meteorological Department (IMD) rainfall data during the principal monsoon season (i.e., June 1 to September 30) from 1976 to 2000 over the states of Himachal Pradesh (HP) and Uttarakhand (UK) in North‐West Himalayan Region (NWH). Considering the inter‐model differences, the multimodel means (MMM) of both of the CORDEX‐SA and CMIP5 models have been compared with the IMD gridded rainfall data set so as to find out the suitable set of models (whether CORDEX‐SA or CMIP5) for assessing the rainfall variability over this region. The CORDEX‐MMM indicates a poor relationship with IMD data compared with the CMIP5‐MMM over both the NWH states of HP and UK. CMIP5‐MMM demonstrates better ability to represent extreme rainfall events as compared with the CORDEX‐MMM. Significant bias has been noted for both of the CORDEX‐MMM and CMIP5‐MMM with respect to the IMD data sets, nevertheless, differences were found to be minimal in case of CMIP5‐MMM in comparison to CORDEX‐MMM. In case of the simulated rainfall from CMIP5‐MMM over both HP and UK regions, RMSE, MAE and bias, all are much smaller than that of the CORDEX‐MMM simulations with respect to the IMD rainfall data. Further to this, CMIP5‐MMM notably performs better in case of delineating extreme cases of inter‐annual variability except CORDEX‐MMM for the state of UK. Results presented here encompass the entire spectrum of summer monsoon rainfall variability, for example, daily, monthly, seasonal, inter‐annual and extreme rainfall over the NWH region. Based on the comprehensive analysis of global and regional models, it is noted that the CMIP5‐MMM has better ability to simulate rainfall pattern during monsoon season compared with the finer resolution CORDEX‐MMM for both the states of UK and HP. Therefore, it is suggested that CMIP5 models may be utilized to investigate the rainfall variability at daily, seasonal and interannual scales over the NWH region for various forcing scenarios rather than the outputs from CORDEX‐SA domain. [ABSTRACT FROM AUTHOR]

Brahmaputra River Basin (BRB), the largest contributor of sediment load in Ganges–Brahmaputra–Meghna delta, is highly vulnerable to future climate change. Several studies assessed the effects of climate change of BRB on river flow but an assessment on sediment load has not been conducted. Changes in sediment load in the future need to be assessed to control and manage sediment flows in large catchments properly. The present study focuses on developing a hydrological and sediment routing model of BRB using the HEC-HMS model to estimate future sediment load together with the flow for the RCP 8.5 climate scenario. Modified Universal Soil Loss Equation and Engelund Hansen method of HEC-HMS have been applied for the sediment transport of BRB. The model has been calibrated using daily runoff for the period 1983–1996 and validated for the period 1997–2010, respectively. The uncertainty in the percentage change in seasonal sediment load during the pre-monsoon season is higher than that of the monsoon season. However, the contribution of the sediment load of pre-monsoon is very much lower than the monsoon season. The percentage changes in mean annual sediment load compared to the baseline period for the 2020s, 2050s and 2080s are 34, 67 and 115%, respectively. HIGHLIGHTS This is the first attempt to assess the future changes in the sediment load of the Brahmaputra River Basin (BRB).; Soil Moisture Accounting (SMA) method was used for simulating continuous rainfall–runoff processes.; The hydrological model was developed using a high-resolution bias-corrected multi-model ensemble RCM.; The percent changes in mean annual sediment load for the years of 2010–2039 (2020s), 2040–2069 (2050s) and 2070–2099 (2080s) are 34, 67 and 115%, respectively.; The uncertainties associated with the projected sediment loads of BRB increase with time in the future.;

Agro-climatic classification systems based on aridity help to distinguish various features of aridity in different agro-climatic regions around the world revealing potentially serious implications for water and agriculture sectors in light of climate change. This study probed historical and projected variations and shifts in agro-climatic zones associated with dispersion in Probability Density Functions (PDF) for daily temperature (Tmax and Tmin) and precipitation extremes over Pakistan. In this study, a single-model RCM simulation and projection from the output of the Coordinated Regional Climate Downscaling Experiment for South Asia (CORDEX-SA) data were used to evaluate these parameters for three-time spans: near future (2011–2040), mid future (2041–2070), and end of century (2071–2100); each under two different Representative Concentration Pathways (RCP 4.5 and RCP 8.5) after bias-correction for the observed baseline data (1971–2000). The data was collected from 49 weather stations. Future projections based on the used Regional Climate Model (RCM) predicted major changes in extremely arid and semi-arid zones of Pakistan in the future. In terms of statistical moments, the highest magnitude change was noted in the median and mean of the Tmax which increased as much as 5.1 °C, and the 90th percentile of the Tmin increased by up to 6.5 °C under the RCP8.5 by the end of this century in arid and the extremely arid zones. Similarly, the greatest magnitude of change in the precipitation was projected to increase more than 3.3 mm/day under RCPs 4.5 and 8.5 in semi-arid zones by the end of the present century. Projected shifts in agro-climatic zones are coupled with associated changes in the statistical moments of the PDFs for drought and extreme precipitation events in agro-climatic zones in response to climate change. [ABSTRACT FROM AUTHOR]

The rainfall during Indian summer monsoon (ISM) is very important for the population living in the Indian sub-continent. The recent Paris climate agreement determined to keep the global mean temperature rise well below 2 °C and pursue efforts to limit it within 1.5 °C. This global temperature rise would influence the ISM mechanism over the Indian sub-continent. This study examines the possible changes in the ISM characteristics at 1.5 and 2 °C specific warming levels (SWLs) with respect to the historical period. This analysis uses a set of 12 regional climate simulations under Coordinated Regional Climate Downscaling Experiments-South Asia (CORDEX-SA). The land as well as the oceans shows a warming signature due to the effect of anthropogenic forcings with much higher warming over land. The increase of global temperature to 1.5 °C (2 °C) SWL leads to an earlier onset of ISM over India by 7 (11) days in the model ensemble. The increasing land-sea temperature contrast gradually increases the strength of the Findlater jet (by 0.5–0.9 m/s), which transports more moisture towards land and causes higher rainfall (increase by 2–10%) over India. The study reveals that under a higher SWL of 2 °C the mean rainfall is augmented as compared to that under 1.5 °C. However, there exists uncertainty in the findings due to inter-model differences especially for the duration of ISM activity and the spatial distribution of rainfall at different SWLs. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) serve as an important tool to perform high resolution climate simulations for a limited area. However, there remains a crucial issue regarding the efficacy of RCMs that whether they are able to produce added value (AV) in representing the regional climatic features compared to their coarse scale driving Global climate models (GCMs). The present study aims at assessing the AV of a set of RCM simulations performed under the Coordinated Regional Climate Downscaling Experiments‐South Asia (CORDEX‐SA) compared with their respective driving GCMs in representing the Indian summer monsoon (ISM) precipitation and its dynamical features. For this purpose, the performance of RCMs and GCMs is compared for the present day climate (1970–2005) against observations and reanalysis datasets in terms of various ISM features, that is, the spatial distribution of precipitation, the evolution of vertical shear of zonal wind and the onset phase of ISM in terms of delayed or early arrival of monsoon. The RCMs show a clear improvement upon their corresponding driving GCMs in simulating spatial features of precipitation distribution that are characteristically associated with ISM. Regionally, the RCMs also show skill in capturing the precipitation amount as a reduction in bias is found compared with their driving GCMs. There is a significant improvement in the simulation of onset dates of ISM by only some of the experiments particularly, RegCM4 driven with IPSL‐CM5A‐LR. The study concludes that the present set of CORDEX‐SA RCM simulations do indeed add value to their driving GCMs for a number of features associated with the ISM precipitation but this value varies with region, driving GCM and the particular feature of ISM under focus. Simulated climatology (1970–2005) of JJAS mean precipitation (mm day−1) by (A) IMD observation, (a1–l1) GCMs and their corresponding (a2–l2) CORDEX‐SA RCMs grouped together in a box. The orographic feature in spatial variability of ISM is evident in IMD—a characteristic which seems to be better captured here by RCMs as compared to their parent GCMs. [ABSTRACT FROM AUTHOR]

In the present study, four different bias correction methods are assessed in improving the precipitation output from a set of two regional climate simulations for 1971–2005 period. The simulations come from Coordinated Regional Climate Downscaling Experiment over South Asia (CORDEX‐SA). The bias correction methods applied here are linear scaling (Scaling), empirical quantile mapping (EQM), power transformation (PTR) and local intensity scaling (LOCI). All the bias correction techniques show skill in improving the model results, but there are clear differences in the degrees of success depending upon the characteristic of precipitation studies—like seasonal mean, seasonal extremes or inter‐annual variability. Most of the methods show equivalently high ability in correcting the seasonal mean precipitation values; however, there also exist residual biases which vary spatially over India. The largest reduction in bias was observed for the frequency of dry days and precipitation on very wet days, which are significantly altered after adjustment by EQM and PTR. It is found that distribution‐based EQM method is more skilful followed by PTR and LOCI in most of the cases including correction of frequency‐based indices (e.g., dry days number and inter‐annual variability). (a) Climatology of JJAS mean precipitation (mm/day) (1971–2005) from (A) IMD, (B) uncorrected values from RCA4, (C–F) same as (B) but bias corrected by different methods, respectively, scaling, EQM, PTR and LOCI. (b) Same as (a) but their biases against IMD. [ABSTRACT FROM AUTHOR]

Precipitation is one of the important climatic indicators in the global climate system. Probable changes in monsoonal (June, July, August and September; hereafter JJAS) mean precipitation in the Himalayan region for three different greenhouse gas emission scenarios (i.e. representative concentration pathways or RCPs) and two future time slices (near and far) are estimated from a set of regional climate simulations performed under Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) project. For each of the CORDEX-SA simulations and their ensemble, projections of near future (2020-2049) and far future (2070-2099) precipitation climatology with respect to corresponding present climate (1970-2005) over Himalayan region are presented. The variability existing over each of the future time slices is compared with the present climate variability to determine the future changes in inter annual fluctuations of monsoonal mean precipitation. The long-term (1970-2099) trend (mm/day/year) of monsoonal mean precipitation spatially distributed as well as averaged over Himalayan region is analyzed to detect any change across twenty-first century as well as to assess model uncertainty in simulating the precipitation changes over this period. The altitudinal distribution of difference in trend of future precipitation from present climate existing over each of the time slices is also studied to understand any elevation dependency of change in precipitation pattern. Except for a part of the Hindu-Kush area in western Himalayan region which shows drier condition, the CORDEX-SA experiments project in general wetter/drier conditions in near future for western/eastern Himalayan region, a scenario which gets further intensified in far future. Although, a gradually increasing precipitation trend is seen throughout the twenty-first century in carbon intensive scenarios, the distribution of trend with elevation presents a very complex picture with lower elevations showing a greater trend in far-future under RCP8.5 when compared with higher elevations. [ABSTRACT FROM AUTHOR]

The ability of an ensemble of five regional climate models (hereafter RCMs) under Coordinated Regional Climate Downscaling Experiments-South Asia (hereafter, CORDEX-SA) in simulating the key features of present day near surface mean air temperature (Tmean) climatology (1970-2005) over the Himalayan region is studied. The purpose of this paper is to understand the consistency in the performance of models across the ensemble, space and seasons. For this a number of statistical measures like trend, correlation, variance, probability distribution function etc. are applied to evaluate the performance of models against observation and simultaneously the underlying uncertainties between them for four different seasons. The most evident finding from the study is the presence of a large cold bias (−6 to −8 °C) which is systematically seen across all the models and across space and time over the Himalayan region. However, these RCMs with its fine resolution perform extremely well in capturing the spatial distribution of the temperature features as indicated by a consistently high spatial correlation (greater than 0.9) with the observation in all seasons. In spite of underestimation in simulated temperature and general intensification of cold bias with increasing elevation the models show a greater rate of warming than the observation throughout entire altitudinal stretch of study region. During winter, the simulated rate of warming gets even higher at high altitudes. Moreover, a seasonal response of model performance and its spatial variability to elevation is found. [ABSTRACT FROM AUTHOR]

An assessment of the projection of near surface air temperature over the Himalayan region from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out for different Representative Concentration Pathway (RCP) scenarios. The purpose of this study is to assess the probable future changes in the mean temperature climatology and its long term trend for different seasons under greenhouse gas forcing scenarios for different seasons till the end of 21st century. A number of statistical measures such as changes in mean climatology, long term trend and probability distribution function have been used in order to detect the signals of changes in climate. Moreover, the associated uncertainties among different model experiments and their ensemble in space, time and different seasons in particular have been quantified. Despite of strong cold bias in the model experiments over Himalayan region (Nengker et al., 2017), statistically significant strong rate of warming (0.03–0.09 °C/year) across all the seasons and RCPs have been projected by all the models and their ensemble. Season specific response towards the warming is indicated by ensemble under future climate while ON season shows comparable magnitude of warming than DJF. Such warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios from RCP2.6 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the trend magnitude between different models describes the uncertainty associated with the model projections and scenarios. A substantial seasonal response to warming with respect to elevation was also found, as DJF season followed by ON portrays highest rate of warming, specifically at higher elevation sites such as western Himalayas and northern part of central Himalayas. The different elevation classes respond differently to the projected future warming under different RCPs and seasons. Such higher warming during DJF may have consequences as changes in the fractional distribution of the solid and liquid precipitation as well as the melting of the glacial deposits with ultimately affecting the streamflow response and water resources in the downstream areas. [ABSTRACT FROM AUTHOR]

An assessment of the projection of minimum and maximum air temperature over the Indian Himalayan region (IHR) from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out under two different Representative Concentration Pathway (RCP) scenarios. The major aim of this study is to assess the probable future changes in the minimum and maximum climatology and its long-term trend under different RCPs along with the elevation dependent warming over the IHR. A number of statistical analysis such as changes in mean climatology, long-term spatial trend and probability distribution function are carried out to detect the signals of changes in climate. The study also tries to quantify the uncertainties associated with different model experiments and their ensemble in space, time and for different seasons. The model experiments and their ensemble show prominent cold bias over Himalayas for present climate. However, statistically significant higher warming rate (0.23–0.52 °C/decade) for both minimum and maximum air temperature (T min and T max ) is observed for all the seasons under both RCPs. The rate of warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios starting from RCP4.5 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the magnitude of trend between different models describes the uncertainty associated with the model projections and scenarios. The projected rate of increase of T min may destabilize the snow formation at the higher altitudes in the northern and western parts of Himalayan region, while rising trend of T max over southern flank may effectively melt more snow cover. Such combined effect of rising trend of T min and T max may pose a potential threat to the glacial deposits. The overall trend of Diurnal temperature range (DTR) portrays increasing trend across entire area with highest magnitude under RCP8.5. This higher rate of increase is imparted from the predominant rise of T max as compared to T min . [ABSTRACT FROM AUTHOR]

An accurate representation of the Indian Ocean Dipole (IOD) is crucial for the reliable projection of Indian summer monsoon rainfall, making it necessary to improve the understanding of the response of the IOD in the warming climate. For the first time, a high-resolution regional earth system model (RESM) over the CORDEX-SA domain is used to investigate the IOD characteristics. The model performance is evaluated in simulating the IOD and associated mechanism. RESM shows a good resemblance in simulating IOD phases (positive and negative). However, the systematic discrepancy is observed in magnitude. Additionally, RESM well represented the positive IOD's inter-event variability. For example, the stronger event dominated by significant cold anomalies over Sumatra with enhanced westward-extended while a moderate event shows weak cooling confined to the region of Sumatra. Additionally, RESM shows potential to distinguish the ENSO and non-ENSO years with more remarkable skill in representing the spatial pattern of SST over IOD region during non-ESNO years than ENSO years. The RESM realistically simulated the IOD amplitude with greater skill than CMIP5/6 models reported in the earlier studies, indicating reliability towards the projection of the Indian summer monsoon. The weaker IOD-ENSO relationship is caused by producing the more significant number of IOD during non-ENSO years. Despite this reliable fidelity, IOD's slightly earlier peak is driven by the early establishment of low-level equatorial easterly wind. This study provided valuable insight into the IOD's different phases, responsible forcings, and limitations of the RESM in accounting for the role of internal climate variability that can be useful for further improvement in the model physics. • A high-resolution RESM is implemented over CORDEX-SA towards improving the IOD characteristics. • RESM shows a good resemblance in simulating IOD phases, associated mechanisms. • Different flavors of positive IOD (inter-event variability) are well captured by RESM. • The realistic IOD amplitude simulation indicates the reliability of Indian monsoon projection. • Early establishment of low-level equatorial easterly wind drives earlier peak of IOD. [ABSTRACT FROM AUTHOR]

For the first time for CORDEX-South Asia, a high-resolution regional earth system model (ROM) is adopted to assess the impact of horizontal resolution (0.22° and 0.11°) in simulating the Indian summer monsoon rainfall (ISMR) and the underlying spatiotemporal variability. ROM at both resolutions bears a close resemblance to observations in simulating the mean precipitation climatology compared to other regional climate models (RCMs) participated in CORDEX- South Asia. ROM shows substantial improvement relative to the ensemble mean of the RCMs included in CORDEX-South Asia. While comparing both simulations with observations, some systematic wet and dry bias over Central India (CI) and Northern Western Ghats is noticed. In general, the wet/dry bias over India is mainly associated with the overestimation/underestimation of the large-scale/convective component. Increasing horizontal resolution from 0.22° to 0.11° significantly adds value in simulating the JJAS mean precipitation by reducing the wet bias over western central India (WCI) and southern peninsular India and dry bias over eastern CI. The reduction in wet/dry bias is mainly associated with suppression/enhancement of the large scale/convective precipitation. This improvement in mean precipitation is partially due to the improved representation of the propagation of mesoscale systems such as boreal summer intraseasonal oscillation (eastward and northward). Despite the above improvements, the wet precipitation bias, particularly over WCI, persists. The weaker Findlater Jet associated with weaker land-ocean thermal contrast caused by the warm sea surface temperature (SST) bias over the western Arabian Sea (AS) suggests that AS moisture transport does not contribute to the wet bias over India. The wet bias is possibly associated with favourable atmospheric conditions (atmospheric instability). • A high-resolution regional earth system model namely ROM is implemented over CORDEX-South Asia. • The impact of horizontal resolution (0.22° and 0.11°) in simulating the summer monsoon characteristics is investigated. • The improved representation of the dynamical and thermodynamical processes yield better monsoon simulation. [ABSTRACT FROM AUTHOR]

The episodes of heat wave events have strengthened in recent decades causing great concern for human health, agriculture and natural ecosystem. In the present study, Regional Climate Models (RCMs) namely, CCAM and RegCM, from Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) are evaluated for simulating heat waves (March–June) for a long-term period (1971 to 2005) over India in comparison with observations from India Meteorological Department (IMD). The statistical analysis (correlation, RMSE, MAE, ECDF) results reveal differences in RCMs in simulating spatial pattern and trends of maximum temperature before bias correction. Variance scaling bias correction is found to remove bias and improve model simulations in capturing temperature variability. An increase in correlation in daily observations from 0.24 to 0.70 and reduction in RMSE from 8.08 °C to 2.02 °C and MAE from 3.87 °C to 2.43 °C after bias correction is observed between model and observation. LMDZ4 and GFDL-ESM2M are found to perform best in simulating interannual variability of seasonal mean maximum temperature with an underestimation of −7.74% and −15.41% which improved significantly to around −1.51% and − 0.78%, respectively after bias correction over India. LMDZ4 and GFDL-ESM2M are also best-performing models in significantly reproducing the heat wave frequency and spatial variability in closer proximity with observations over India amongst all models after bias correction. Over NW and western regions, the LMDZ4 and GFDL-ESM2M ensemble models successfully capture the increasing trend of 0.2 events/year and 0.4 events/year accordance to IMD and IITM criteria, respectively. However, the ACCESS1.0, CNRM-CM5 and CCSM4 ensemble experiments overestimated heat waves by ±40 events in most sub-divisions in India. Over the central Indian regions, the ACCESS 1.0 and CNRM-CM5 model output show a negative trend of −0.2 events/year and large spatial variability possibly due to model associated uncertainties. Overall the results show an improvement in capturing maximum temperature and heat waves across the regions of Indian sub-continent in the bias-corrected downscaled CORDEX-SA ensemble RCMs than without bias-corrected output. The study suggests a way forward to assess RCMs performance and uncertainty in extreme weather analysis in future projections. • First study to evaluate performance of eight CORDEX-SA regional climate models for simulating heat waves over India. • Variance scaling bias correction method removes bias and improves model simulations in capturing temperature. • Heat wave analysis using IMD and IITM criteria. • LMDZ4 and GFDL-ESM2M best performing model in simulating heat wave frequency and spatial variability. [ABSTRACT FROM AUTHOR]