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Understanding the regional hydrological response to varying CO2 concentration is critical for cost-benefit analysis of mitigation and adaptation polices in the near future. To characterize summer monsoon rainfall change in East Asia in a changing CO2 pathway, we used the Community Earth System Model (CESM) with 28 ensemble members in which the CO2 concentration increases at a rate of 1% per year until its quadrupling peak, i.e., 1468 ppm (ramp-up period), followed by a decrease of 1% per year until the present-day climate conditions, i.e., 367 ppm (ramp-down period). Although the CO2 concentration change is symmetric in time, the amount of summer rainfall anomaly in East Asia is increased 42% during a ramp-down period than that during a ramp-up period when the two periods of the same CO2 concentration are compared. This asymmetrical rainfall response is mainly due to an enhanced El Nino-like warming pattern as well as its associated increase in the sea surface temperature in the western North Pacific during a ramp-down period. These sea surface temperature patterns enhance the atmospheric teleconnections and the local meridional circulations around East Asia, resulting in more rainfall over East Asia during a ramp-down period. This result implies that the removal of CO2 does not guarantee the return of regional rainfall to the previous climate state with the same CO2 concentration.

District-wise monsoon (June - September) rainfall trends over Konkan-Goa and Marathwada meteorological subdivisions of India were analyzed in this study using rainfall data for the period 1951-2018. Mean monsoon rainfall is the highest over Konkan-Goa and the lowest over the Marathwada subdivision in the state of Maharashtra, India. Linear regression, Mann-Kendall test, and Sequential Mann-Kendall were used to identify trends in rainfall. Higher variability in the rainfall trends was noticed over the districts of Marathwada (-25% to +25%). The total number of below and above normal rainfall years (20 to 25) were comparatively equal for both the subdivisions; however, the number of deficient and excess rainfall years was more for the districts of Marathwada. Most of the districts in Marathwada showed declining trends in the summer monsoon rainfall. Significant decreasing trends were noticed in June month over Latur (-11 mm/decade) and Osmanabad (-7mm/decade) districts of Marathwada. The decadal rate of change in the overall monsoon rainfall for the Konkan-Goa subdivision varies between -15mm (Mumbai City) to -59mm (Raigad) and +2mm (Beed) to -30mm (Nanded) over the districts of Marathwada. The study has been carried out to understand the behavior of rainfall patterns post 1980, but the results were similar to the overall 68 years’ rainfall trends. In general, insignificant mixed seasonal trends and insignificant negative trends were observed over most of the districts of both the subdivisions, which decline the consideration of monsoon vagaries and droughts due to rainfall change as a manifestation of climate change.

In the present paper, the variabilities and long-term trends of summer monsoon rainfall for different intensity bins (dry, low, moderate, high, very high, and extreme) are studied for five homogeneous regions, namely Northeast India (NEI), Northcentral India (NCI), Northwest India (NWI), Westcentral India (WCI), and Peninsular India (PI) for 118 years (1901–2018). The study was carried out based on gridded rainfall data from the India Meteorological Department (IMD). The rainfall characteristics such as number of rainy days, percentage contribution, and periodicity of rainfall intensity classes are analysed and found to be different in different homogeneous regions. The long-term trend (1901–2018) of total rainfall showed a significant increasing trend (19.9 mm decade−1) in NEI and significant decreasing trends in NCI (9.6 mm decade−1) and PI (4.9 mm decade−1). Analysis on rainfall intensity indicates a significant increasing trend for high, very high, and extreme classes in NEI, a significant increasing trend for dry, and a decreasing trend for moderate and very high classes over NCI and PI. From correlation analysis among the homogeneous regions, it is found that the rain events in different intensity classes show different relationships, which indicate the regional heterogeneity in rainfall characteristics. It is also important to note that an increase in rainfall contribution from very high and extreme classes was found over NEI, NWI, and WCI in the multidecadal period of 1991–2018, while NCI showed a decrease during this period; however, in NCI, a drastic increase for these intensity bins is distinct during the 1961–1990 multidecadal period. In addition to the trends and variabilities, we also explored spatial heterogeneity of different rainfall intensity categories, and found remarkable differences from one homogeneous region to another.

Seasonal prediction of Indian summer monsoon rainfall has been considered as one of the important factors to decide the social and economic aspect of India because of its multi-sectorial dependencies. This study evaluates the performance of seven state-of-the-art GCMs in simulating the summer monsoon rainfall on a seasonal scale over the period of 1982–2008 using the GCM reforecasts. The rainfall simulated by the models is compared with the IMD observed rainfall dataset at 0.25° × 0.25°. Preliminary analysis of spatial pattern and statistics shows that the models IITM-CFSv2, NCEP-CFSv2 and ECMWF are some of the prominent models that capture the seasonal rainfall pattern and possess good skill. ECMWF performs very well in simulating the rainfall pattern as well as the rainfall intensities. Comprehensive statistical analysis such as standard deviation ratio and skill scores concludes that the IITM-CFSv2 produces the rainfall pattern as well as the variability of the summer monsoon better than its counterparts. The multi-model simple mean also tends to improve with the addition of IITM-CFSv2. Though the rainfall trend and variance simulated by IITM-CFSv2 is quite in agreement with the observed, there lies a significant dry bias over the north-west India. The mean simulated rainfall is quite less with the CFSv2 models. Though the IITM-CFSv2 simulates lesser rainfall at all the four-lead times, it is quite capable in capturing the rainfall variability. The models ECMWF and GFDLA04 are well performers in terms of mean rainfall estimates whereas the models CFSv2 is better in terms of reproducing the rainfall variability.

The influence of sea-level pressure (SLP) of Arabian Sea on variability of summer monsoon over northern Pakistan has been discussed in this paper. This study will examine the impact of SLP of Arabian Sea on the summer monsoon of northern Pakistan using composite and correlation analysis, principal component analysis (PCA), and singular value decomposition analysis (SVDA). The correlations of SLP index and first principal component of SLP with the regional rainfall over the monsoon season are respectively − 0.43 and − 0.44 (both significant at 5% level). The first leading SVD mode is found to be the most important because it is alone explaining 95.6% of the total square covariance. Moreover, canonical correlation analysis (CCA) has been applied on the regions that have correlations significant at 5% level on heterogeneous correlation maps to construct forecasting equation using canonical correlations. The correlation between the left and right expansion coefficients for the first leading canonical mode is 0.83 which is higher than in the case of the first leading SVD mode. Furthermore, the composites show that the physical mechanisms are consistent with empirically determined relationships.

El Nino Southern Oscillation (ENSO) affects Indian Summer Monsoon (ISM) at interseasonal to interannual time scales. Given the heterogeneity in rainfall patterns over India, we have revisited the linkages between El Nino and ISM for the period 1600–2016 in this study. Our analysis based on the instrumental, paleo-proxy, model and satellite data sets show significant spatiotemporal variation in the ISM in response to El Nino. We observed strengthening of ISM over the northeast India (NEI) in conjunction with El Nino events. We suggest that the decadal trends in ISM over the NEI and central India (CI) were controlled by the long term variation in strength of El Nino events. We observed spatial variation in isoGSM derived rainfall and its oxygen isotopic composition (δ18O) in response to El Nino events. We further verified our observations from isotope proxy palaeo-records. Two high resolution speleothem (cave deposits) records of reconstructed ISM during 1600–2008 confirmed the inverse relation of rainfall patterns over CI (negative) and NEI (positive) during moderate to strong El Nino periods. These speleothem records infer a long-term pause in the El Nino (or stronger La Nina) like conditions, which were persisted during 1625–1715 and favored the stronger (weaker) rainfall over the CI (NEI). Furthermore, speleothem records showed stronger El Nino events during 1715–1760 causing significant reduction and enhancement in rainfall amount over CI and NEI, respectively.

The Indian summer monsoon as a major component of the global climate system has received renewed attention in recent years. There are many instances of years with floods (strong monsoon) or drought (weak monsoon) during which India as a whole receives excess or deficient seasonal rainfall, respectively. Monsoon variations, particularly if they are unanticipated, impart significant economic and social consequences. On the other hand, an accurate long-lead prediction of monsoon rainfall can improve planning to mitigate the adverse effects of the inter-annual variability of the monsoon. One such attempt is made in this research to establish empirical relationships between sea-surface temperature (SST) and summer monsoon rainfall over Central India. India is surrounded by Indian Ocean and the annual cycle of SST in the Indian Ocean is crucially important in the distribution of precipitation over the Indian subcontinent. The study identifies four pockets in the Indian Ocean which can be considered as precursors to summer monsoon rainfall. Correlation analysis with lags in months was carried out to establish association between the SST over these pockets and summer monsoon rainfall. The study revealed that the relationship between the two variables has undergone phase-change, and has oscillated between inverse and direct correlation values. Besides, the study also reveals the relationship between SST and monsoon rainfall for different meteorological subdivisions of Central India, which further broadens the scope for researchers to evaluate the impact of coupled land-ocean and air interactions for different micro-spatial units of India.

This study investigates the tele-connection of the southeast Asian monsoon systems by comparing the summer monsoon (June to September) rainfall variability between Pakistan and south Korea. The daily data sets (1981-2014) of rainfall of Pakistan and south Korea are utilized to explore the possible link. Thedata products of the National Centers for Environmental Predictionand National Center for Atmospheric Research (NCEP/NCAR) were also used for the understanding of the large-scale atmospheric environments. The patterns of summer monsoon rainfall on a daily basis between Pakistan and south Korea followed to eachotherthroughout the year. Sub-seasonal differences of the summer monsoon revealed that July is the wettest month in both countries. The large-scale atmospheric environment of higher geopotential height revealed that the Tibetan high and the western north Pacific subtropical high are showing positive anomalies during positive phases over south Asia and east Asia,respectively. The anomalies of zonal wind are negative during positive phase and adversein the negative phase between 20-40oN. The reduced westerly is interpreted as the seasonal variation and moving of jet streams from the east Asian route. The Tibetan high, northwestern Pacific subtropical high and the east Asian jet stream have reliable and sufficient linkage between the Pakistan and south Korea summer monsoon system.

The present work reports a study on the behavior of the time series of rainfall during the summer monsoon (June–September) in northeast India. It has been observed through hypothesis testing that although a normal distribution fits the time series, no persistence forecast is possible as indicated by the low value of the Lag-1 autocorrelation coefficient (0.041). A linear regression over time is not a good fit. Subsequently, the intrinsic behavior of the time series has been explored through Hurst exponent (H) computed using rescaled range analysis. The value of the Hurst exponent is in the range of 0 < H < 0.5, and from this range, it could be understood that long-term switching is expected to exist between high and low values in adjacent pairs. However, when the Hurst exponent value is being low, the summer monsoon rainfall over northeast India has been thought of as characterized by volatility and roughness. Finally, the connectivity of the El Nino-Southern Oscillation (ENSO) to the Indian summer monsoon rainfall has been briefly discussed in light of the long-term behavior.

The South West Monsoon rainfall data of the meteorological subdivision number 6 of India enclosing Gangetic West Bengal is shown to be decomposable into eight empirical time series, namely Intrinsic Mode Functions. This leads one to identify the first empirical mode as a nonlinear part and the remaining modes as the linear part of the data. The nonlinear part is modeled with the technique Neural Network based Generalized Regression Neural Network model technique whereas the linear part is sensibly modeled through simple regression method. The different Intrinsic modes as verified are well connected with relevant atmospheric features, namely, El Nino, Quasi-biennial Oscillation, Sunspot cycle and others. It is observed that the proposed model explains around 75% of inter annual variability (IAV) of the rainfall series of Gangetic West Bengal. The model is efficient in statistical forecasting of South West Monsoon rainfall in the region as verified from independent part of the real data. The statistical forecasts of SWM rainfall for GWB for the years 2012 and 2013 are108.71 cm and 126.21 cm respectively, where as corresponding to the actual rainfall of 93.19 cm 115.20 cm respectively which are within one standard deviation of mean rainfall.

The variability in Indian summer monsoon rainfall (ISMR) has an immense socioeconomic impact on India, especially over the monsoon core region. In this study, a thorough analysis of the ISMR (June to September—JJAS) has been performed for East India (EI), a part of the monsoon core zone, and for its four subdivisions (Odisha, Chhattisgarh, Jharkhand, and Bihar). The long-term climatology, variability, and trends of various categorical rainfall events as defined by India Meteorological Department (IMD) have been analyzed by using IMD high-resolution (0.25° × 0.25°) gridded dataset for 116 years (1901–2016). Also, the percentage contribution of various rainfall events to the total seasonal rainfall and wet days is estimated. To get the changes in the frequency of various dry spells, rainfall events, and associated seasonal rainfall, study period is divided into two halves, earlier period (P1: 1901–1958) and recent period (P2: 1959–2016). The results suggest that the percentage contribution of medium-intensity rainfall events (7.6–64.4 mm/day) to the total ISMR is higher (~ 72%) followed by high-intensity rainfall events (~ 15%) and low-intensity rainfall events (12%), while most percentage contribution to the total number of wet days is from low-intensity rainfall events (~ 54%) followed by medium-intensity rainfall events (~ 43%) and high-intensity rainfall events (~ 3%). In the recent period (P2), the ISMR significantly decreased over the central part of EI because of the significant decrease in the frequency of low and medium-intensity rainfall events and wet days. The remarkable decrease of wet days leads to an increase in the short (1 week) and long-duration (> 2 week) dry spells in P2. And at the same time, high-intensity rainfall events significantly increased over EI in the recent period (P2) and led to an increase in the flood situation over this region. The profound relation of deficit rainfall of EI with ENSO is getting weak in the P2. This study is certainly useful in determining the effects on various sectors because of the variability and changes in various categorical extreme rainfall events, short and long-duration dry spells during summer monsoon time over EI. Also, this study may assist the risk management sectors in acclimating advanced technologies for a sustainable future in the changing climate of the present global warming era.

The current research aimed to evaluate the predictive skill of statistically downscaled National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) data in simulating the Indian summer monsoon rainfall (ISMR) for the period of 1961–2005 over the individual homogeneous monsoon regions of India (HMRI). For the purpose, five models are selected, as these models (in GCM) have shown better performance in the simulation of ISMR by the researcher. The spatial characteristics and statistical scores (annual cycle, percentage bias, Taylor score, probability distribution function) are used to evaluate the performance of each model in simulating rainfall over land points of individual HMRI. In the spatial analysis, it seems that models of NEX-GDDP can simulate the ISMR, pretty well in comparison to APHRODITE (observation), and show a moderate to significantly high correlation (grid point) over each of the HMRI particularly to core monsoon region, except over few parts of PI. The Taylor statistics suggest that the model CanESM performs very well over the regions of PI, NWI, and WCI. The models MPI-ESM-LR and NorESM perform well in simulating the ISMR over CNI, followed by ACCESS, CanESM, and CCSM4. The models have varying bias in predicting the rainfall; however, ACCESS does perform well and shows the minimum bias (ranges from ~ 1 to ~ 14% only) among others. The models CanESM and NorESM (except over CNI) performed relatively better. The NEX-GDDP models overcome the global climate models (GCMs) in the retrospective simulation of ISMR over the land points of India. It is concluded that the models have good predictability of JJAS rainfall but unable to catch daily rainfall variability.

This work investigates the boreal-summer intraseasonal variability (ISV) of the precipitation over the lower reaches of the Yangtze River basin (LYRB) during 1979–2016, based on daily Climate Prediction Center global precipitation data. The ISV of the summer monsoon rainfall over the LYRB is mainly dominated by the lower-frequency 12–20-day variability and the higher-frequency 8–12-day variability. The lower-frequency variability is found to be related to the northwestward-propagating quasi-biweekly oscillation (QBWO) over the western North Pacific spanning the South China Sea (SCS) and Philippine Sea, while the higher-frequency variability is related to the southeastward propagating midlatitude wave train (MLWT). Moreover, not each active QBWO (MLWT) in the SCS (East Asia) can generate ISV components of the precipitation anomaly over the LYRB. The QBWO can change the rainfall significantly with the modulation of mean state precipitation, while the quasi-11-day mode mainly depends on the intensity of the MLWT rather than the mean precipitation change. These findings should enrich our understanding of the ISV of the East Asian summer monsoon and improve its predictability.

Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN.

All India summer monsoon rainfall (AISMR) shows a declining trend since 1953. Hence, the period 1953–2015 is considered for this study. The deviations of the summer monsoon (SM) rainfall in the recent period (1983–2015) from that of the previous period (1953–1982) have been evaluated for individual months and whole SM season using India Meteorological Department (IMD) gridded rainfall. The changes in the SM rainfall in the recent 3 decades with respect to the changes in the thermal, moisture, and circulation patterns over India and neighborhood are studied. The relationships between the winds over the Somali jet core region and the SM rainfall over India are quantified. Enhancement of rainfall activity is seen over a few subdivisions mainly Jammu & Kashmir and parts of Northwest India where the western disturbance interaction and the meridional wind component of low level jet (LLJ) have been pronounced. The decline in rainfall over major part of India in the recent 33 years is associated with weakened low level circulation, decrease in the relative humidity (RH) over the Bay of Bengal, less frequency of cyclonic disturbances formed in the Bay of Bengal, decrease in the genesis potential parameter as well as north-south sea level pressure gradient between Northern and Southern Hemispheres. The inconsistency of warming rates between Northern India and Southern India and its adjoining areas in the layer, 600–200 hPa induces reduced SM circulation. Further, the Walker and Hadley circulations specify a decline in the convective activity in SM season over the Indian mainland.

The objective of the current study is to investigate and evaluate the annual and seasonal rainfall trends and patterns of the Punjab province, Pakistan during the 1981-2015. The spatial patterns and temporal trends were identified through the Modified Mann Kendall test. Finding revealed that 13 weather stations of Punjab province have shown the statistically significant decreasing trend of annual and summer monsoon rainfall during the study period. From further investigation, the rainfall during the summer monsoon period (JJAS) found to be increased by 12.45%, similarly the rainfall during the whole of year be also increased by 18.75%. The significant decreasing trends observed with the higher percentage change in annual and monsoon rainfall for the stations of Sialkot and Lahore in the northern and central Punjab. The empirical evidences suggested that northern Punjab gets more moisture as compared the Southern and Western Punjab. This study suggests intensive empirical research in the future to evolve further spatio-temporal trends in the rainfall pattern of Punjab.

Prediction of the Indian summer monsoon rainfall is essential for the country because the food production depends on the rainfall during the summer monsoon season. India being an agricultural country, its economy also depends on the monsoon rainfall. Various institutes across the world give global prediction. An experimental prediction based on the numerical simulations produced by North American Multi-Model Ensemble (NMME) has been attempted for the initial conditions of May to understand the strength, variability, predictability, and associated changes in the Indian summer monsoon. This study is particularly aimed to find out the efficacy of NMME models over the Indian region during the southwest monsoon season. The analysis shows that all the nine models underestimate the climatology. A mismatch exists between the spatial patterns of model climatology as compared to the observed climatology. It is found that models underestimate the inter-annual variability of the precipitation as compared to the observed ones. This can be attributed to the overestimation of sea surface temperature-Indian summer monsoon rainfall (SST-ISMR) response. This might lead to poor performance of the model in terms of precipitation prediction. The spatial correlation shows varying correlation pattern as compared to the observed one. However, almost all models have positive correlation over the peninsular India. The basic idea of MME approach is to generate a single prediction from the predictions from different models. The MME approach shows positive correlation over the peninsular and central India.

In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44g (g1/4g50gkm), 0.22g (g1/4g25gkm), and 0.11g (g1/4g12gkm) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12gkm grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM-RCM modeling chain., Geoscientific Model Development, 14 (8), ISSN:1991-9603, ISSN:1991-959X

The rainfall data in South West Monsoon season of the meteorological subdivision number 3 of India consisting of the states Assam and Meghalaya is subjected to decomposition into seven subjected hierarchical Intrinsic Mode Functions. The first and second functions are understood as non-linear part where the other part indicates the linear part of the data. The non-linear part is modeled with Generalized Regression Neural Network and the other part is modeled applying multiple linear regression method. The atmospheric features such as El Nino, Quasi-biennial Oscillation, Sunspot cycle, and others are well represented with the different Intrinsic Mode Functions. 75% of year to year Inter annual variability of the subdivision is explained from the model developed. The model is efficient as verified from a part of real data unused in the model. The statistical forecasts of SWM rainfall for Assam and Meghalaya for the years 2012 and 2013 are 14,871 mm and 10,520 mm respectively, whereas corresponding to the actual rainfall of 14,886 mm and 9927 mm respectively which are well within one standard deviation of mean rainfall.