Explore the vast range of titles available.

Current knowledge suggests a drought Indian monsoon (perhaps a severe one) when the El Nino Southern Oscillation and Pacific Decadal Oscillation each exhibit positive phases (a joint positive phase). For the monsoons, which are exceptions in this regard, we found northeast India often gets excess pre‐monsoon rainfall. Further investigation reveals that this excess pre‐monsoon rainfall is produced by the interaction of the large‐scale circulation associated with the joint phase with the mountains in northeast India. We posit that a warmer troposphere, a consequence of excess rainfall over northeast India, drives a stronger monsoon circulation and enhances monsoon rainfall over central India. Hence, we argue that pre‐monsoon rainfall over northeast India can be used for seasonal monsoon rainfall prediction over central India. Most importantly, its predictive value is at its peak when the Pacific Ocean exhibits a joint positive phase and the threat of extreme drought monsoon looms over India. Plain Language Summary Monsoon brings rain over India. But some years are droughts. These drought monsoon years are historically associated with warmer sea surface temperatures (SSTs) in the eastern Pacific and cooler SST in the northern Pacific. This motivated scientists to predict drought monsoons when we observe a warm eastern and cold northern Pacific Ocean. However, in some years, the monsoon is not drought despite the SST anomalies in the Pacific suggesting so. We find that, in such years, rainfall over northeastern India during pre‐monsoon months is often excessive. So we argue that when the Pacific Ocean state suggests a drought monsoon over India (central region) but if pre‐monsoon rainfall over northeastern India is excessive, then we can rely less on the drought signal of the Pacific Ocean. Key Points Pre‐monsoon rainfall over northeastern India is a potential indicator of false alarms of monsoon drought over central Indian region Association between northeastern India pre‐monsoon rainfall and monsoon rainfall over central India oscillates multidecadally Sea surface temperature anomalies in the Pacific are a key driver of pre‐monsoon rainfall over the northeastern India

We analyse the performance of global climate models of 6th generation of Coupled Model Intercomparison Project (CMIP6) in simulating climatological summer monsoon rainfall over India, interannual variability (IAV) of all-India summer monsoon rainfall (ISMR) and its teleconnections with rainfall variability over equatorial Pacific and Indian Oceans. The multimodel ensemble mean (MME) of 61 CMIP6 models shows the best skill in simulating mean monsoon rainfall over India compared to the MMEs of 6th generation atmosphere-only models (AMIP6) and the previous generations of Atmospheric and Coupled Model Intercomparison Projects (AMIPs and CMIPs). Systematic improvement and reduction in bias are evident from lower to higher AMIPs/CMIPs. Still, there exists dry bias over a narrow region of the monsoon zone of central India besides wet and cold bias over the surrounding oceans. The persistence of errors in atmosphere-only models hints that the source of errors could be with atmosphere models. Fifteen CMIP6 models selected through objective criteria, perform the best in simulating mean monsoon, IAV of ISMR, the strong inverse relationship between ISMR and Boreal summer El Niño-Southern Oscillation (ENSO), and the inverse relationship between all-India rainfall and north–west tropical Pacific rainfall in June. Several models reproduce the dipole structure of Equatorial Indian Ocean Oscillation (EQUINOO) with the centres over western and eastern equatorial Indian Ocean. But, ISMR-EQUINOO relationship in many of them is opposite to the observed. Our analysis implies the need for capturing ISMR-EQUINOO link to improve the simulation of IAV of ISMR which is crucial for reliable monsoon prediction and projection.

Indian Summer Monsoon Rainfall (ISMR) is one of the most well-documented areas of hydrometeorology; however, the processes associated with ISMR are not well understood. This attributes to the complexities associated with ISMR at multiple spatio-temporal scales. This further results in inconsistencies across the literature to assess the impacts of global warming on the monsoon, though this has huge relevance as a huge population of South Asia is dependent on the same. Here, we review and assess the existing literature on the Indian monsoon, its variability, and its trajectory in a warming scenario. We further synthesize the literature on its impacts on the hydrology of major river basins in South Asia. We also identify a few research questions, addressing which will add value to the understanding of the Indian monsoon and the associated water cycle. We have highlighted that there is a significant lack of understanding of how different large-scale and regional factors affect ISMR at different timescales. These impacts, in turn, get translated into hydrology and water sector in India. There is a need to know where we stand to combat the impacts of climate change on ISMR, which can be translated to adaptation by policy-making processes and water management practices in India.

This study presents the rainfall statistics, conditional probability structure and statistical dependence of rainfall amount of several gauging stations located around Peninsular Malaysia, namely Subang, Senai and Kota Bharu. Daily rainfall measurements for all stations were collected from the Department of Meteorology, Malaysia are long and reliable, with at least 40 years of data. The average annual rainfall estimated for Kota Bharu, Subang and Senai are 2,627 ± 574 mm, 2581 ± 399 mm and 2499 ± 340 mm, respectively. The effect of monsoon seasons on the monthly rainfall amount is evident in this study. The most significant variation in the average monthly rainfall is noticed for Kota Bharu. There was some variation in the average monthly rainfall for Subang and Senai. The conditional probability structure for t-consecutive wet and dry days show that the multi-day events are time-dependent. For example, the probability of occurrence for a single dry day is 0.458, 0.453 and 0.553 and increased significantly to 0.696, 0.780 and 0.817 for 8-consecutive dry days at Subang, Senai and Kota Bharu, respectively. The dependence of rainfall amount was analyzed using the auto correlation function (ACF). The range of ACFs estimated for all stations were very low, i.e. 0.0050 to 0.0209, 0.0093 to 0.0857 and 0.0633 to 0.3700 for Subang, Senai and Kota Bharu, respectively. This result shows that the rainfall amounts are independent of each other. Overall, the analysis shows that the east coast region received more annual rainfall with higher variability, as compared to the central and south parts of Peninsular Malaysia. Additionally, the total amount of rainfall observed for all stations varies spatially and temporarily.

Prominent multidecadal rainfall trends and trend reversal points in the Indian summer monsoon rainfall during 1901–2020 across the four homogeneous regions of India have been examined beginning at the district level. Employing a robust rainfall pattern identification methodology, three significant rainfall trend reversal events have been identified during 1930s, 1960s, and 1980s. During the 1930s, central and northeast India witnessed a shift from increasing to decreasing rainfall trends, while the south peninsula experienced the reverse, resulting in a pronounced north-south asymmetry in rainfall pattern over India. In the 1960s, south peninsula and northwest India exhibited a reversal in rainfall trend from increasing to decreasing, with an opposite trend in northeast India, resulting in an east-west asymmetry in the rainfall pattern. Unlike the 1930s and 1960s, the rainfall trend reversal during the 1980s occurred over all four rainfall homogeneous regions. The three regions (south peninsula, central, and northwest) in India experienced rainfall trend reversal from decreasing to increasing trends, while the northeast experienced the opposite trend reversal, establishing an east-west asymmetry in the rainfall pattern. In terms of geographical extent, the rainfall trend reversal in the 1980s is the most prominent event during the last 120 years of Indian rainfall history as the maximum geographical area (~ 50%) experienced the rainfall trend reversal during this period. In terms of magnitude of rainfall amount variation, the rainfall trend reversal during the 1930s is the most prominent as more than 30% of the area had significantly higher (or lower) rainfall than the long-term average. Temporal changes are observed in the identified spatial asymmetry of rainfall pattern indicating that rainfall homogeneous regions in India must have changed over time.

The South Asian High (SAH) is the upper-level anticyclonic-circulation over the Tibetan Plateau, strongly impacting Asian monsoon rainfall during boreal summer (June–August). The present study aims to understand the dynamic linkage between Indian summer monsoon rainfall (ISMR) and the SAH from four seasonal models (CANCM4, NEMO, CANSIP, and CFSv2). Observations indicate that the northwest–southeast (I NW–SE ) index of SAH is strongly correlated (~ 0.62) with ISMR whereas the east–west (I EW ) index is negatively correlated (~ − 0.57). All the models reasonably capture this relation between ISMR and SAH indices. The positively regressed rainfall anomalies are (90%) significant during I NW–SE years and attributed to the strong cold sea surface temperature anomalies over the equatorial eastern Pacific (i.e., La Niña) and positive vorticity associated with the strong cyclonic-circulation over monsoon region. Similarly, significant negative rainfall anomalies are identified during I EW years, strongly associated with El Niño patterns and negative vorticity anomalies over monsoon region. Unlike the CFSv2 and observations, the CANCM4, NEMO, and CANSIP models show strong positive (negative) regressed rainfall anomalies in I NW–SE (I EW ) years over India, and mainly due to strong linkage with El Niño and the Indian Ocean Dipole in the models. Overall, the high resolution CFSv2 performs better than other models for mean rainfall and teleconnections between ISMR and SAH. Of the three models, the CANCM4 performs better in capturing dynamic circulations such as vorticity, velocity potential, stream function etc. The study highlights the seasonal model’s ability to capture the linkage of ISMR and SAH indices and helps understand rainfall variability.

The Indo-Gangetic Plain (IGP), the source of grains for around 40% Indian population, is known as the breadbasket of India. The Indian Summer Monsoon Rainfall (ISMR) plays a vital role in the agricultural activities in this region. The rapid urbanization, land use and land cover change have significantly impacted the region’s agriculture, water resources, and socioeconomic facets. The present study has investigated the observed and regional modeling aspects of ISMR characteristics, associated extremes over the IGP, and future perspectives under the high-emission RCP8.5-scenario. Future projections suggest a 10–20% massive decrease during pre-monsoon (March–May) and earlier ISM season months (i.e., June and July). A significant 40–70% decline in mean monsoon rainfall during the June–July months in the near future (NF; 2041–2060) has been projected compared to the historical period (1986–2005). An abrupt increase of 80–170% in mean monsoon rainfall during the post-monsoon (October–December) in the far future (FF; 2080–2099) is also projected. The distribution of projected extreme rainfall events shows a decline in moderate or rather heavy events (5 or more) in NF and FF. Further, an increase in higher rainfall category events such as very heavy (5–10) and extremely heavy rainfall (5 or more) events in NF and FF under the warmer climate is found. However, the changes are less prominent during FF compared to the NF. The mean thresholds for extremely heavy rainfall may increase by 1.9–4.9% during NF and FF. Further, the evolution patterns of various quantities, such as tropospheric temperature gradient (TG), specific humidity, and mean sea level pressure, have been analyzed to understand the physical processes associated with rainfall extremes. The strengthening in TG and enhanced atmospheric moisture content in NF and FF support the intensification in projected rainfall extremes over IGP.

Historically, El Niño events have consistently signalled below-average monsoon rainfall in India excluding years like 1997. Despite 2023 being an El Niño year, India experienced normal monsoon seasonal rainfall (-6% of the Long Period Average: LPA) with above-average rainfall (+ 13% of LPA) during July and September but unadorned deficit rainfall in August (-36% of LPA). Thus, the complex relationship of El Niño with the Indian summer monsoon rainfall (ISMR) is apparently evident during summer 2023. Monthly rainfall variations starkly challenge conventional hypotheses, necessity for a profound understanding of the dynamics behind them. During June 2023, El Niño triggered a robust midlatitude upper-level Rossby wave over the north central Pacific. The propagation of wave energy along the westerly jet is further evidenced by wave activity flux. Specifically in August, this waveguide induced upper-level cyclonic circulation cantered around north China with strong northerly wind anomalies in its western flank supports the low-level cyclonic circulation with a southward tilt to the south of Japan in response to the equivalent Barotropic structure. This coupling intensified the anomalous cyclonic circulation over the WNP, bolstered by potent El Niño influence and the phase of the Madden–Julian Oscillation (MJO). These anomalies facilitated moisture transfer from the monsoon region to the WNP, as a result excess (deficit) rainfall is evident over the WNP (ISMR) region due to strong large-scale ascending (descending) in August 2023. During July and September, in contrast, the absence of a midlatitude Rossby wave, the prevailing positive Indian Ocean Dipole (IOD) and MJO offset this El Niño induced rainfall deficiency. El Niño impact on monsoon rainfall is significant, however, this observational study highlights the pivotal roles of IOD, MJO, and midlatitude circulation patterns. Their interplay creates substantial rainfall variability, emphasizing the complexity of El Niño-monsoon teleconnections.

Pakistan summer monsoon rainfall consists of a large portion of the local annual total rainfall, and in the recent monsoon seasons, prolonged periods of anomalous rainfall and excessive flooding have appeared in Pakistan. A full understanding of the monsoon rainfall variability is important for the sustainable development of the country. Based on multiple data analyses and the weather research and forecasting model, the potential impact of Tibetan Plateau (TP) heating on the interannual variability of Pakistan monsoon rainfall is investigated. It is observed that a significant negative relationship exists between the thermal forcing over the southeastern TP and Pakistan monsoon rainfall in July–August. Both the data analyses and model sensitivity experiments identify that the TP heating drives a Rossby wave response in the upper atmosphere characterized with an anticyclonic anomaly over the southern TP but a cyclonic anomaly to the north. This dipole pattern of anomalous circulation induces an evident upper-level convergence over Pakistan, corresponding with remarkable vertical sinking motion. Meanwhile, in the lower troposphere, the TP heating causes anomalous westerly wind along the Himalayas over the northern India continent. Such westerly anomaly further induces less water vapor transport into Pakistan from the Bay of Bengal. Therefore, both the dynamic and thermodynamic processes regulated by positive TP heating are not beneficial for the occurrence of monsoon rainfall in Pakistan. This study proposes a new potential mechanism in which TP heating acts as a driver of Pakistan monsoon rainfall variability on interannual time scales.