Accurate subseasonal to seasonal (S2S) forecasts of Indian Summer Monsoon Rainfall (ISMR) are vital for agricultural planning, water resource management, and disaster risk reduction. Conventional post-processing techniques for S2S General Circulation Model (GCM) forecasts predominantly rely on linear methods, which often exhibit limited predictive skill. In this study, we investigate the use of deep learning–specifically, U-Net convolutional neural networks (CNNs)–for improving probabilistic ISMR forecasts. We apply U-Net-based bias correction to three state-of-the-art S2S GCMs: GEFSv12, ECMWF, and IITM ERPv2, training the U-Net models to calibrate tercile probabilities of weekly accumulated rainfall at lead times of 1 to 4 weeks for the monsoon season (June-September) over India. Our results show that the U-Net enhances probabilistic forecast skill, consistently outperforming the baseline Extended Logistic Regression (ELR) approach. Using hindcast data from 1989 to 2022, we observe a statistically significant improvement in weeks 3–4 probabilistic forecast skill, measured by the Ranked Probability Skill Score (RPSS). The extent of improvement, however, varies with the amount of training data available for each GCM, and can reach a 2 p.p. increase, from 2

The Equator-to-Pole temperature gradient sustains the zonally axisymmetric Hadley-type thermally direct circulations and the midlatitude thermally driven jet, as well as baroclinicity. The current study focuses on the role of Arctic warming on the interannual variability modes of the Hadley circulation through modulation of the equator-to-pole temperature gradient. Some observational studies indicate the strengthening of the annual mean and boreal winter Hadley cell. Other studies indicate the role of different climate-teleconnection modes on the Hadley cell. However, the association of the Arctic amplification trend and the Hadley cell is not well understood. The empirical orthogonal modes of interannual variability of the Hadley cell are first identified using the meridional mass stream function. It was found that the first mode of the Hadley cell is more closely associated with other climate variability modes, such as the Atlantic Multidecadal Oscillation, and exhibits weak correlation with Arctic amplification. However, the second mode shows a correlation of 0.43 with the Arctic amplification Index and a strong correlation with both Hadley cell strength and meridional velocity at 200 hPa. Furthermore, regression analysis of both the Arctic amplification Index and the second mode with eddy fluxes shows a similar trend pattern of an increasing transient eddy momentum flux divergence (EMFD) in the subtropics. The intensification of transient EMFD leads to weakening zonal winds and reduced baroclinicity in the subtropics. Furthermore, the eddy momentum flux from the mid-latitudes triggers the Rossby waves to the subtropics, reinforcing EMFD and the second mode. Support for the mechanism comes from the sensitivity experiments, by forcing the model for a period of a minimum sea ice. The result shows intensification of EMFD and overturning circulation with poleward shift of the subtropical jet. Thus, the analysis shows the impact of Arctic amplification through the eddy-driven dynamics of the Hadley cell.

Cold wave (CW) events over India are usually observed during the boreal winter months, November to February. This study proposes an objective criterion using the actual, departure from normal and the percentile values of the daily gridded minimum temperature (Tmin) data for the monitoring of the CW events over the Indian region and also checks its usefulness in a multi-model ensemble extended range prediction system. The large-scale features associated with these CW events are also discussed.The CW-prone region has been identified by utilizing this proposed criterion and considering the number of average CW days/year for the entire study period and recent decades. By calculating the standardized area-averaged (over the CW-prone region) Tmin anomalies time series, the CW events are identified from 1951 to 2022. Analyzing the temporal variability of these events, it is seen that there is no compromise in the occurrences of the CW events, even under the general warming scenarios. It is found that the long CW events (>7 days) are favoured by the La-Nina condition, and short CW events (≤7 days) are favoured by the neutral condition in the Pacific. Also, the blocking high to the northwest of Indian longitude with the very slow movement of the westerly trough to the east is found to be associated with the long CW events. In contrast, in the case of short events, the blocking high is not so significant. The multi-model ensemble prediction system is found to be reasonably skilful in predicting the CW events over the CW-prone region up to 2-3 weeks in advance with decreasing confidence in longer leads. Based on the forecast verifications, it is noticed that this forecasting system has a remarkable strength to provide an overall indication about the forthcoming CW events with sufficient lead time despite its uncertainties in space and time.

Super Cyclone Gonu (1-7 June 2007) Awas the first recorded Category 5 tropical cyclone in the Arabian Sea and one of only five Super Cyclonic Storms(SuCS) Areported by the India Meteorological Department(IMD) over the past 35 years. Forming shortly after the onset of the Indian Summer Monsoon, Gonu's rapid intensification during a typically unfavorable period for cyclogenesis represents a remarkable deviation from climatological norms. This study examines the atmospheric and oceanic factors that enabled its development using ERA5 reanalysis, NOAA OISST, and IMD Best Track data. Analyses show that anomalously high sea surface temperatures, elevated mid-to upper-tropospheric humidity, and low-to-moderate vertical wind shear created a highly favorable thermodynamic environment. A southward intrusion of midlatitude westerlies disrupted the Tropical Easterly Jet, enhancing upper-level divergence and supporting vigorous deep convection. Vertically integrated moisture transport revealed strong southwesterly inflow into the cyclone core, with concurrent diversion of moisture from the Indian subcontinent, temporarily delaying monsoon progression. Rainfall analyses from GPCP dataset indicate intense and asymmetric precipitation, with significant orographic enhancement over coastal regions. Organized low-level vorticity and persistent moisture convergence sustained Gonu's structural integrity until landfall. These findings highlight the interplay of ocean-atmosphere interactions, large-scale circulation, jet stream variability, and precipitation dynamics in early-season cyclogenesis and emphasize the importance of integrated forecasting for extreme tropical cyclones in a changing climate.

The transient eddies in the atmosphere are short-lived, moving disturbances prominent over mid-latitude. Transient eddy transport enables the exchange of mass, energy, and moisture between extratropical and tropical regions. Based on observations, about 40% of the rain that falls in northern India during the summer monsoons is influenced by transient eddy heat and momentum fluxes. During these rainfall cases, the four-stage cycle of transient eddy heat and a momentum feedback process exists. Global-scale circulation anomalies are generated due to their forcing on the mean flow. This impact of transient eddies on mean flow is referred to as eddy-eddy feedback, commencing in around 22 days. On a daily scale, the enhancement of rainfall over the Western Ghats and north-western India is linked with upper tropospheric poleward transient eddy heat flux transport and equatorward transient eddy momentum flux transport. On a quasi-biweekly scale, however, the transport direction reverses. Additionally, this rainfall pattern is governed by the meridional passage of monsoon intraseasonal oscillation phases (MISO, tropical mode) and the zonal passage of wave number 7-8 patterns (Rossby wave, extratropical mode). Interactions of tropical-extratropical modes are associated with this eddy -eddy feedback that drives the hemispheric upper-tropospheric circulation patterns. This includes wave generation, propagation, and the dissipation of waves away from the source region. The hindcast skill analysis of subseasonal to seasonal scale models, namely Extended Range Prediction Application to Society (ERPAS) and Global Seasonal Forecast version 5 (GloSea5), shows that the models can predict northern Indian rainfall associated with eddy-mean flow interactions at 1-week lead times. After a week, the skill of both models diminishes under the influence of transient eddy transport. The monsoon circulation is more consistent and predictable in both models when transient eddies are absent. A theoretical understanding of the dynamical feedback of upper-tropospheric transient eddies is crucial for improving rainfall prediction.

The Quasi-Biennial Oscillation (QBO) influences the static stability and wind shear over the equatorial upper-tropospheric regions and lower-stratospheric regions, and hence the convections of underlying areas. This study evaluates the concurrent relationship between QBO phases and the Indian Summer Monsoon (ISM). QBO is identified by the Empirical Orthogonal Function analysis of equatorial zonal wind bounded by 10°S-10°N over 100 − 10 hPa. Since the QBO affects the tropical troposphere, it is hypothesised that QBO can influence ISM through upper-level wind circulation, particularly the tropical easterly jet (TEJ). The easterly phase of QBO (EQBO) is associated with strong easterlies (westerlies) at 200 hPa (850 hPa), enhancing vertical zonal wind shear, strengthening the TEJ, and increasing upper-level divergence and lower-level convection—favourable conditions for a strong ISM. On the contrary, the westerly phase of QBO (WQBO) exhibits opposite features, signifying weak monsoon conditions. Temperature and static stability anomalies further support these findings. The QBO effect extends to the monsoon intraseasonal variability as well. The active spells are more persistent and spatially extended during EQBO, while break spells are shorter and weaker. Contrarily, the breaks are of higher amplitude and long-lived during WQBO. The robustness of these relationships is confirmed by ENSO-neutral cases, highlighting the independent role of QBO in modulating the subseasonal to seasonal variability of ISM. These findings emphasize the potential of incorporating QBO signals into monsoon prediction frameworks, aiding in the differentiation of QBO and ENSO-driven influences.

Subseasonal predictions with a time scale of 2-4 weeks, which fills the gap between the weather and seasonal forecasts, are limited by the uncertainties arising from the initial conditions as well as the model physics. Therefore, to develop an efficient subseasonal prediction system, both these uncertainties need to be addressed. With this background, a multi-physics multi-ensemble approach has been adopted to develop a competent second-generation subseasonal prediction system at the Indian Institute of Tropical Meteorology (IITM), Pune, India. The first-generation prediction system developed at IITM is run operationally at the India Meteorological Department and has useful skills for up to two weeks. A combination of physics perturbations and initial condition perturbations with a total of 18 ensemble members is present in the system. This system has been experimentally run since May 2022. The hindcast runs during 2003-2018 are also made on-the-fly. The initial results indicate a considerable improvement in the forecast skill compared to its predecessor and have reasonable deterministic prediction skill for up to three weeks. The system could provide skilful prediction of the subseasonal variations during the two contrasting monsoon seasons 2022 (above normal) and 2023 (below normal) 2-3 weeks in advance.

Global warming has significantly increased the risk of heat waves (HWs) globally, with India being particularly vulnerable during the summer months (March-June; MAMJ). This study investigated the critical relationship between Indian summer monsoon rainfall (ISMR) and the occurrence of premonsoon HWs in subsequent years across the Indian subcontinent. It has been hypothesized that droughts during the ISMR could lead to more frequent HWs in the following MAMJ period. Using the Indian Meteorological Department's (IMD) gridded observed surface air daily maximum temperature (Tmax) dataset for the period 1951–2023, we analyzed the climatic patterns, interannual variability (IAV), and coefficient of variation (CV) of Tmax across India. The analysis compares two distinct periods: 1951–1999 (P1) and 2000–2023 (P2), with focus on Tmax trends and HW duration, distinguishing between short-duration HWs (SHWs, 2 days) and long-duration HWs (LHWs, 5 days or more). A key purpose of this study is to examine the relationship between the preceding all India summer monsoon rainfall (AISMR) and the occurance of various types of HW in the subsequent premonsoon season. In particular extreme AISMR events, such as droughts or excess rainfall, influence HW occurrence. The findings reveal a significant rise in Tmax across many regions of India during the MAMJ period, with the highest temperatures (> 37 °C) observed in northwestern, central, and eastern coastal areas. Northern India, particularly the Himalayan region, exhibits a greater interannual variability in Tmax, with June showing the most pronounced fluctuations. The study also highlights an increase in the frequency and intensity of HWs, especially in central and southern India, with the Chandigarh-Haryana-Delhi region recording the highest occurrences. A critical finding is the strong inverse relationship between the AISMR and conditions in the subsequent premonsoon season. Specifically, drought in the antecedent AISMR results in reduced soil moisture, which is strongly associated with higher premonsoon Tmax and an increased frequency of extreme heat events across India, particularly in regions prone to severe heat during this season. Drought conditions during AISMR are closely linked to higher HW frequencies in the following summer, especially in the central, northeast-central, and east-coastal regions. The frequencies of HW days, SHWs, and LHWs are significantly greater in years following AISMR droughts than in those following excess rainfall, indicating that drought years are more likely to lead to widespread HW activity. Despite the overall warming trends, some regions, such as the Indo-Gangetic Plain and parts of the Himalayan region, show cooling trends, although these trends are less widespread. The onset of the monsoon in June tends to reduce the intensity and spatial extent of warming, particularly in the central and eastern coastal regions, although significant HW trends persist in northwestern India and along the east coast. This study underscores the crucial role of AISMR in influencing HW events across India and highlights the need for adaptive strategies that account for the interactions between monsoon rainfall and HW risk, providing valuable insights for mitigating the impacts of HWs in the context of global warming.

A comprehensive assessment of the forecast skill of various meteorological indices over the Indian region from different numerical weather prediction (NWP) models is lacking in the literature. In this study the performance of four NWP models, namely Global Ensemble Forecast System (GEFSv12), European Center for Medium Range Forecasting (ECMWF), Climate Forecast System (CFSv2) and Indian Institute of Tropical Meteorology (IITM) towards forecasting of precipitation, temperature and associated meteorological indices, is evaluated at short to medium timescales across the Indian region. Further, the effect of ocean atmospheric (OA) oscillations on the precipitation/temperature forecast skill from the different NWP models is also assessed. Results show that the NWP models are better in predicting the meteorological indices than the quantitative forecasts. The ECMWF model was found to be the best for PCP forecasting with CFSv2 performing poorly. For temperature the GEFSv12 model performance was the lowest, compared to rest of the models. The models show poor skill in forecasting monsoon season precipitation compared to non-monsoon and the temperature forecasts from the NWP models are particularly poor for the northern basins. Skilful temperature forecasts are observed in the Northwestern, Indo-Gangetic and Central basins for the CFSv2 and ECMWF models. The forecast skill of precipitation indices are higher in the northwestern, central and Indo-Gangetic basins compared to the rest. The skill of precipitation indices, namely rainy days, extreme rainy days and consecutive wet days, is higher during the monsoon seasons while the prediction skill of consecutive dry days is higher during the non-monsoon season. OA analysis revealed that the ENSO phases have dominant effect on the forecast skill of the precipitation only. The temperature and meteorological indices forecasts are not affected significantly by the OA phases. The outcomes of this study have implications towards irrigation scheduling and water resources management decision making in India. Quantitative and qualitative intercomparison and assessment of short to medium range precipitation and temperature forecasts over the Indian region is done using multiple NWP models. The NWP models are better at predicting qualitative forecasts (meteorological indices) than quantitative forecasts across the Indian region. The ECMWF model was found to be the best for precipitation forecasts while the GEFSv12 performed poorly for temperature forecasts. Ocean-atmospheric analysis revealed that the ENSO phases have dominant effect on the forecast skill of precipitation only. image

Global warming increases the risk of heatwaves (HWs) globally. In India, HWs during the summer (March-June; MAMJ) are characterized by prolonged high temperatures, exacerbated by low soil moisture. Speculation suggests that droughts during the Indian summer monsoon (ISM), which provides 80% of India's annual rainfall, may lead to more HWs in the following MAMJ period. In this research, an examination is carried out on the climatic patterns, inter-annual variability (IAV), and coefficient of variation (CV) of maximum temperatures (Tmax) throughout MAMJ across the Indian subcontinent using India Meteorological Department's (IMD) gridded observed Tmax dataset covering 1951 to 2023. The dataset is divided into two periods: an earlier period (1951–1999, P1) and a recent warming period (2000–2023, P2). This study compares Tmax between these periods and evaluates HW duration using IMD criteria, distinguishing between short-duration HWs (SHWs, lasting 2 days) and long-duration HWs (LHWs, lasting 5 days or more). Additionally, it explores the relationship between preceding All India Summer Monsoon Rainfall (AISMR) and various HW types, while analyzing the impact of extreme AISMR events (such as drought or excess rainfall) on heatwave occurrences. This study thoroughly examines how Tmax and HWs are distributed across India, shedding light on notable variations in Tmax patterns and HW occurrences. It finds a clear rise in Tmax across various regions, accompanied by an increase in the frequency of HW days, particularly evident during the MAMJ. The study emphasizes the crucial role of AISMR in shaping HW events, highlighting that drought conditions during AISMR are closely linked to a higher chance of experiencing above-normal HW frequencies. This study is very useful in determining the effects on various sectors in planning of adaptation techniques through appropriate strategies for a sustainable future over India in the present global warming era.

What is the role of soil moisture in maintaining the land ITCZ during the active phase of the monsoon? This question has been addressed in this study by using ERA5 reanalysis datasets, and then we evaluate the question in the CFS model-free run. Like rainfall, soil moisture also show intraseasonal oscillation. Furthermore, the sub-seasonal and seasonal features of soil moisture are different from each other. During the summer monsoon season, the maximum soil moisture is found over western coastal regions, central parts of India, and the northeastern Indian subcontinent. However, during active phases of the monsoon, the maximum positive soil moisture anomaly was found in North West parts of India. soil moisture also play a pre-conditioning role during active phases of the monsoon over the monsoon core zone of India. When it is further divided into two boxes, the north monsoon core zone, and the south monsoon core zone, it is found that the preconditioning depends on that region's soil type and climate classification. Also, we calculate the moist static energy (MSE) budget during the monsoon phases to show how soil moisture feedback affects the boundary layer MSE and rainfall. A similar analysis is applied to the model run, but it cannot show the realistic preconditioning role of soil moisture and its feedback on the rainfall as in observations. We conclude that to get proper feedback between soil moisture and precipitation during the active phase of the monsoon in the model, the pre-conditioning of soil moisture should be realistic.

Heat waves (HWs) in India during March–June and from 1951 to 2023 are thoroughly analysed in this work, emphasising trends, decadal variations, and related large-scale features. Average HW days per decade and anomalies are computed using HW criteria based on high-resolution maximum temperature (Tmax) data. The findings indicate a notable increase in HW occurrence in the central, southeast, and northwest regions after the year 2000. Month-wise analyses reveal detailed patterns, showcasing increased HW days in non-traditionally hot months, like March in southern regions. This suggests an intensification of extreme summer conditions over both time and across different regions. Examining the spatial HW trends exposes a notable increase in total HW days/year over northwest, central and south-eastern regions, while few others witness decreasing trends. The study reveals significant increasing trends in the total number of HW days in the two HW-prone regions, Northwest (NW) and Southeast (SE), from 1951 to 2023, where HW spells have also become more persistent. Three types of HW spells are analysed: NW-spells and SE-spells, defined by area-averaged daily Tmax exceeding 43 °C and 40 °C, respectively, for six consecutive days, and NWSE-spells, where HW periods overlap between the two regions. The analysis of large-scale characteristics associated with these HW spells emphasises the possible role of oceans and atmospheric variables in HW patterns. These findings highlight the importance of improving predictive capabilities for HWs. To this end, the extended range prediction system version 2 (ERPSv2) is introduced in this study, assessing its subseasonal prediction skill. The results demonstrate that ERPSv2 performs better than its predecessor, ERPSv1, particularly with a three-week lead time. Validation through a case study on the June 2023 HW disaster showcases ERPSv2’s efficacy in forecasting real-time events with a four-week lead time. Incorporating ERPSv2 adds a practical dimension, enhancing HW predictions and facilitating timely responses to extreme heat events, crucial for public health measures and climate resilience planning in the face of escalating HW occurrences.

Documentation of the skill of a prediction system and its comparison with those of leading modelling centres are crucial in model development. This facilitates understanding the limitations of the existing prediction system and aids in its improvement. The current study compares the extended range prediction skill of the Indian Institute of Tropical Meteorology (IITM) generated real-time forecast with that of the UK Met Office (UKMO) forecast during the boreal summer monsoon season. It is found that both models suffer from biases in the climatological mean state of the monsoon. IITM forecast possesses a skill comparable to UKMO coupled seasonal forecast as compared to the observation in the first two weeks leads over most of the meteorological subdivisions during the monsoon months of June to September. However, at longer leads, the UKMO model outperforms the IITM model, which could be credited to its enhanced skill in predicting the monsoon intraseasonal oscillations and the better representation of monsoon variability at the intraseasonal time scale.

Cold wave (CW) events over India are usually observed during the boreal winter months, November–February. This study proposes an objective criterion using the actual, departure from normal and the percentile values of the daily gridded minimum temperature (Tmin) data for the monitoring of the CW events over the Indian region and also checks its usefulness in a multi-model ensemble extended range prediction system. The large scale features associated with these CW events are also discussed. Utilizing this proposed criterion and considering the number of average CW days/year for the entire study period and recent decades, the CW prone region has been identified. By calculating the standardized area-averaged (over the CW prone region) Tmin anomalies time series, the CW events are identified over the period 1951–2022. Analyzing the temporal variability of these events, it is seen that there is no compromise in the occurrences of the CW events even under the general warming scenarios. It is found that the long CW events (> 7 days) are favoured by the La-Nina condition and short CW events ( ≤ 7 days) are favoured by the neutral condition in the Pacific. Also, the blocking high to the north-west of Indian longitude with very slow movement of westerly trough to the east is found to be associated with the long CW events, whereas in case of short events the blocking high is not so significant. The multi-model ensemble prediction system is found to be reasonably skilful in predicting the CW events over the CW prone region up to 2–3 weeks in advance with decreasing confidence in longer leads. Based on the forecast verifications it is noticed that this forecasting system has a remarkable strength to provide an overall indication about the forthcoming CW events with sufficient lead time in spite of its uncertainties in space and time.

Approximated and simplified real-atmospheric process impact in physical parameterization is a primary correspondent of biases in the model, particularly for extreme events. The present study discusses how event genesis in the small and large-scale quintessential environment is incongruously simulated within a set of multiple convection parameterizations. Despite a few inherent errors, most of the selected convective parameterization schemes could indicate 10-15 days in advance the Uttarakhand heavy rains resulted from large-scale background interaction. The runs without any convection scheme, followed by new-Tiedtke and BMJ schemes, outperform in this case. Further, almost all schemes except new-Tiedtke flunked for the case of Mount-Abu flood originated from relatively local-scale interaction even from 5-day advance initialization. Results are further extended for a few other cases using best performers of both extreme events and new-Tiedtke found to be more efficient. The better representation of convection (especially the shallow) and low clouds in this scheme makes it superior to other schemes for simulating extreme precipitation events.

An assessment of a multiphysics multimodel ensemble (MPMME) strategy is provided to simulate the critical aspects of the Indian summer monsoon and its intraseasonal variability. Using various physics combinations of Climate Forecast System (CFS) and its atmospheric component Global Forecast System (GFS), a 15‐year hindcast for May–October is generated. Three different convective parametrizations, simplified Arakawa–Shubert (sas), revised deep‐convection sas (nsas), and revised sas with modified shallow‐convection (nsas_sc) are coupled with two microphysics schemes Zhao and Carr (zc) and Ferrier. Spatiotemporal characteristics of predicted Indian summer monsoon climatology and 20–70‐day periodic intraseasonal oscillations (ISOs) are evaluated using observations. MPMME members reproduce the overall characteristics of the seasonal mean, but they have significant biases over different regions. Pattern correlations reveal that CFS_nsaszc performs best among MPMME in simulating the observed characteristics of rainfall ISOs and providing significant ISO forecast up to pentad 3 lead. A diagnostic based on the vorticity budget equation during strong convective events (SCEs) associated with ISOs is used to understand better the mechanism of northward‐propagating ISOs and the responsible factors that develop a vorticity tendency to the north of convection maxima. The tilting term in the vorticity equation shows northward propagation and leads precipitation maxima by about a week over the Bay of Bengal. Vertical shear of mean zonal winds and meridional gradients of vertical winds are found to be essential in developing vorticity tendency. SCEs are better represented in CFS than in GFS. Notably, along with CFS_nsaszc, two CFS_sas members capture the occurrence of SCEs reasonably well. However, errors in vertical shear of mean zonal winds are remarkably high after pentad 2 lead in CFS_sas and GFS, explaining their relative weakness in simulating ISOs during June–September. This study demonstrates that the MPMME strategy could utilize individual physical schemes' strengths to provide better subseasonal forecasts.

In this study, we have analyzed the role of the initialization in the forecast of the monsoon onset phase beyond 10 days lead time (i.e., in the extended range) to understand the impact of displacement or shift in initial conditions in the extended-range forecast. Two displacement errors are considered: (a) the initial error arising due to a change in land surface initial conditions and (b) the initial error arising due to a change in the number of observations. For the first part (a), we have analyzed and compared the difference in prediction skills in the United Kingdom Met Office (UKMO) GloSea5 forecasts run with two different land surface initial conditions (IC) configurations. In one configuration, the IC is prepared using a monthly land surface climatology; in the other configuration, it is based on daily land surface reanalysis. In the other part (b), we used the Indian Institute of Tropical Meteorology's Climate Forecast System (IITM_CFS) model with two different ICs (NCEP and NCMRWF differing in the number of observations over the Indian land region). Both runs indicate a shift in the initial condition, which manifests as displacement error. UKMO and IITM_CFS runs have the same land surface model when the corresponding twin experiments are compared in (a) and (b). Analysis of the initial displacement errors from these runs indicates that improving the realism in the land surface initial conditions can effectively modulate or change the surface meteorological fields in the prediction model during the onset phase. The result shows that the rotational and divergence components of the surface winds differ in the two sets of runs. Results also indicate that the difference in surface initialization manifests as differences in rotational and divergent kinetic energy. This could lead to a difference in the forecast of monsoon onset rain. Further analysis also suggests that the local land surface initial condition error, in addition to an error in large-scale teleconnections, affects the monsoon onset forecast and its prediction skill in the extended-range time-scale.

Investigating the trends and changes in rainfall over vulnerable regions is of huge importance in this global warming era. The present study intensively investigates the rainfall over the Indian state, Andhra Pradesh (AP), and its 13 districts using a high-resolution (0.25°×0.25°) gridded rainfall analysis dataset from India Meteorological Department (IMD) for the study period of 118 years (1901-2018). For this, normality, homogeneity, persistence, and change-point tests are performed and changes in the district-level rainfall in the present global warming period (1991-2018) as compared to the pre-global warming period (1901-1990) is also analyzed. The results suggest that the long-term average annual rainfall over AP is 882 mm and most of the rainfall is contributed by the monsoon (55.7%) and the post-monsoon rainfall (32.8%). The coefficient of variation is low (high) during monsoon (winter). The coastal region receives more rainfall than the inland districts. The post-monsoon rainfall over AP is more consistent than in other seasons, and the persistence is only during the southwest monsoon season. The southwest monsoon and post-monsoon rainfall have increased (by about 10%) over most of the districts in the recent period. The Nino3.4 region SST (South Oscillation Index; SOI) has a significant negative (positive) relationship with southwest summer monsoon rainfall in most of the districts. The relationship of Nino 3.4 SST and DMI is strikingly similar for post-monsoon and has significantly weakened in recent decades. This study is useful for proper planning and mitigation measures for the agricultural and water resources sector at the district level over AP in this global warming era.