Flipping of temperature and precipitation trends over the Indian subcontinent due to diametrically opposing influence of GHGs and aerosols

Despite significant development in the Earth system models (ESMs) and releases of several coupled model intercomparison projects (CMIPs), the evolving patterns of Indian summer monsoon rainfall and its future trajectory is still uncertain, with low confidence in its direction. This could be because of differential impacts from increasing greenhouse gas (GHG) and aerosol concentrations. We found that the observed pre-2000 (1951–2000) declining monsoon was likely attributed to the increasing aerosol concentrations. On the contrary, the reported revival of post-2000 monsoon rainfall is due to GHG dominance. These are spatiotemporally consistent with individual CMIP Phase 6 (CMIP6) ESM simulations with GHG and aerosols separately. Similar results were obtained for temperature in India, which showed no to low warming signal in pre-2000 due to aerosol-driven cooling. The dominance of GHG impacts has increased India’s warming trend in post-2000. This research highlights a notable trend in temperature and precipitation across the Indian subcontinent during the past two decades, emphasizing the dynamic character of climate change explained by contrasting anthropogenic influences, including GHGs and aerosols.


Introduction
The Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6) highlights that since 1850, the global average temperature has steadily risen.There has been approximately a 1.1 • C increase in temperature compared to pre-industrial period due to anthropogenic greenhouse gas (GHG) emissions (IPCC 2021), with a faster rate of warming starting from the 1970s.As reported in IPCC in 2021, with an increase in the frequency and magnitude of positive temperature anomalies, the past five years have been the hottest on record globally (IPCC 2021).Furthermore, since 1950, it is evident that global mean land precipitation has also increased.The anthropogenic forcers including GHGs and aerosols are the main drivers of the past and future changes in the climate as well as the extremes, as extremes are part of the climate system (Wang et al 2023).Aerosols, due to their radiative properties and microphysical effects, have the potential to influence the regional land-atmospheric interactions and the monsoonal rainfall characteristics (Hansen et al 2000, Rosenfeld 2000, Jacobson 2001, Ramanathan et al 2001, Nakajima et al 2007, Niyogi et al 2007, Huang et al, 2014, Guo et al 2015), with opposite effects on the climate by absorbing and scattering aerosols.Several studies have comprehended aerosols among scattering & absorbing components and the aerosol-radiation (Babu and Moorthy 2002, Lau and Kim 2006, Ramachandran and Cherian 2008, Sanap and Pandithurai 2015, Saha and Ghosh 2019) & aerosol-cloud interactions (Warner and Twomey 1967, Twomey 1974, Ramaswamy et al 2001, Lau and Kim 2006, Bollasina et al 2011, Matsui et al 2011, Wang 2013).The reduction in aerosols results in climate warming (Wang et al 2023) and due to global dimming, the earth surface cools and it eventually weakens the Asian monsoon (Ramanathan et al 2005).The aerosol cooling effect masks a significant amount of warming caused by GHGs.Bollasina et al (2013) comprehended that the Indian monsoon onset shifted by 10-20 days earlier in the late 20th century, likely due to the contribution of anthropogenic aerosols.Aerosol emissions dominated the monsoon trends, especially during the 1950-1970(Guo et al 2015)).Comparison of the coupled model intercomparison project (CMIP5) model's 20th-century experiments highlights the Indian summer monsoon rainfall (ISMR) trends with GHG-only runs showing increased rainfall, while aerosol-only runs indicate decreased rainfall (Saha and Ghosh 2019), indicating the influence of aerosols in monsoon reduction and strengthening of monsoon in GHG dominated situations.Modifications in aerosol emissions play a crucial role in both multidecadal variability and nearterm climate changes (Collins et al 2017, Szopa et al 2021).
As an agronomic society, India mainly depends on the Indian summer monsoon and its seasonality for cultivation and harvesting.The recent AR6 report (IPCC 2021) explicitly showed that since the 1950s, the observed change in hot extremes and heavy precipitation have highly increased in the South Asian domain, which encompasses the Indian subcontinent.The global mean annual surface air temperature has been increasing in the recent century (Jones and Moberg 2003) and from 1980 onwards, greater warming is observed over land (Houghton et al 2001).The maximum temperatures substantially contributed to the rise in the mean temperature of India, whereas the minimum temperature remained essentially not much significant (Kothawale and Rupa Kumar 2005).Anthropogenic activities have enhanced the GHG concentrations since around 1750 and it caused the climate to warm.The observed warming is mainly attributed by the anthropogenic activities, with GHG warming partly reduced by aerosol cooling (IPCC 2021).India is one of the largest emitters of aerosols in the Asian continent (Wang et al 2021).Decreased aerosol loading since the 1990s has led to accelerated warming of hot extremes in some regions (IPCC 2021).After 1950s, the northern central region experienced a decrease in summer monsoon rainfall.According to the literature, this significant drying trend is attributed to the reduction in the landsea temperature contrast due to the (i) GHG induced warming in the western and southern Indian Ocean While debates exist on the exact role of aerosols on Indian subcontinent and on the driving factors of contrasting multi-decadal trends in precipitation, literature did not focus on understanding the resultant of contrasting role of major factors on the changing climate of India.Some comparisons with the current literatures and the discrepancies are mentioned in the supplementary document.It is also important to understand their collective role on rainfall and temperature, and the existence of common causal pathways leading to temperature and precipitation changes.We hypothesize that during pre-2000 time period, dominance of aerosol constraints India's land warming, and supresses the land ocean-contrast and monsoon rainfall.Dominance of GHG impacts over the aerosols in post-2000 increases land warming and resulted in a reversal of monsoon rainfall trends.We tested our hypothesis with the recent observed trends and with aerosol and GHG only simulations by the CMIP6 models.The following sections provide the data used in this study with a brief description of the methodology and investigation results.2).The yearly variations in temperature and rainfall patterns over the Indian region are examined.Regional and global temperature, regional rainfall and global temperature anomaly patterns are analysed, employing a five-year running mean to reduce pattern unevenness.Aerosol and CO 2 variations are studied (figures S1 and S2) to understand their influence on temperature and precipitation changes, using normalized values.The justification for considering year 2000 is shown with change point analysis in figure S3.Mean anomalies of temperature and precipitation, overlaid by 850 hPa wind anomalies for 2001-2020, are examined with a base period of 1959-2000.The CMIP6 model analysis supports results from the observational dataset.Multi-model means of the model simulations are also computed and shown.Land area, obtained using land-sea masks, is restricted to the Indian mainland.Further normalisation of the data is done where the anomalies are divided by the standard deviation, with the total time period data and split for the respective time period of analysis followed by trend analysis.Model-based spatial trends for NAT, AA, and GHG forcings are examined for Indian mainland.Similar analysis is conducted for the homogenous climatic (based on rainfall) zones (Parthasarathy and Mooley 1978) classified as peninsular, west-central, central northeast, northwest, and northeast (see figures S6, S9 and S10).Jammu and Kashmir and northeast hilly regions are excluded.Core monsoon zone analysis is also presented in supplementary results (figures S7 and S8).Spatial trends for surface air temperature and precipitation are derived using the nonparametric Mann-Kendall test at a 95% confidence level.The CMIP6 historical simulations incorporate forcings from both natural phenomena (such as volcanic eruptions and solar variability) and anthropogenic influences (including CO 2 concentration, aerosols, and land use) spanning the period from 1850 to 2014 (Srivastava et al 2020).A comparative analysis done with CMIP6 historical simulations and SSP 245 simulations w.r.t.observations are shown in the supplementary document.Individual impacts of different forcings may not add up to the impacts from the total forcings.Section 3 details the results and discussion of the study.

Temporal variation of regional temperature and precipitation
The annual variation of temperature and precipitation patterns over the Indian mainland are examined in figure 1. Figure 1(a) depicts the declining (slope −0.002) yearly variation in regional temperature throughout the years 1951-2000 with p-value of mean temperature as 0.37.However, after 2000, when global warming accelerated, the temperature began to increase sharply with a slope of 0.011 with mean temperature p-value 0.19.Similar to figure 1(a), figure 1(b) indicates that precipitation in the Indian subcontinent diminished (slope −0.538) from 1951 to 2000 with p-value 0.76 and then resumed (slope 7.105) after 2000 with p-value 0.65, indicating a revival of rainfall.This is similar to the finding reported by Jin and Wang (2017).We considered year 2000 as a change point in the present study, for homogeneity in analysing both temperature and precipitation.
In order to better comprehend the imprints of global climatic changes on the regional scale, the five-year running mean of annual regional v/s global anomaly (normalised) of both temperature and precipitation is represented in figure 2. Regional v/s global temperature anomaly shows an increasing trend, particularly after the year 2000.Prior to 2000, the mean temperature had a consistent change; interestingly, after 2000, it escalated even more (figure 2(a)).The slope of has increased from 0.235 to 0.920 (∼1), indicating a consistent increase in the global and regional temperature pattern.Similar variations can be observed in both the maximum and minimum temperatures (figures 2(b) and (c)).Therefore, in the recent era, this increase in regional temperature is more evident along with the global temperature variations.The regional annual monsoon anomaly v/s the global temperature anomaly showed a similar pattern, depicting the monsoon revival after the year 2000 (figure 2(d)).Until the year 2000, there was a decreasing trend in anomalous rainfall variation and temperature anomaly variation, indicated by a slope of −0.183.However, post-2000, there was a noticeable shift, with rainfall starting to increase in alignment with temperature variations.This consistent increasing trend can be linked to the rise in GHG emissions dominating the aerosol emissions.Literature has mentioned that the recent two decades have witnessed more extremes like heatwaves influence in the case of temperature (Dash et al 2009, Murari et al 2015) and heavy precipitation events (Roxy et al 2017).Therefore, the increase in both temperature and precipitation in the recent era may be convincingly justified.

Changes in GHG and aerosol patterns
In order to explore the influence of anthropogenic factors, changes in the GHG and aerosol distribution over the study region are examined.CO 2 and AOD are considered as the proxy for GHG and aerosols, respectively.The annual increase in both global CO 2 concentrations and regional (Indian mainland) CO 2 emissions are depicted in figures S1(a) and (b), where the increase began in the early 1970s (IPCC 2021) and is still being continued.The increasing trend in normalized CO 2 values over time suggests a rise in CO 2 concentrations with in a normalised change of −2 to 2 for both global and regional analyses.Similar variation in AOD shows decreasing trend for global analysis and conversely an increasing trend in concordance with the GHG is shown in the case of regional analysis.The Indian subcontinent and surrounding regions are subject to heavy loading of absorbing aerosols (Lau and Kim 2006).A rise in CO 2 concentration can serve as a primary indicator of elevated levels of GHGs in the atmosphere.In this perspective, it is crucial to comprehend the significance of analysing the impact of aerosols and GHG on the temperature and precipitation regimes.
As a developing country, India's emissions may increase in the future.Currently, the Indo-Gangetic plains (IGPs) and the Indus valley in the Indian region have the highest concentration of aerosol loading with AOD 0.4-0.6 at 555 nm (figure S2), due to increased anthropogenic activities and dense human settlements.The aerosol concentration is more prominently seen over the tropical region.In this study, AOD is used as an indicator of aerosol concentration.This presence of aerosols cools the air in that region, reducing the convection and the reduced convection leads to reduced rainfall.Therefore, our research supports the notion that the concentration and influence of GHGs are rising because it has increased precipitation over the past two decades despite rising aerosol concentrations.The observational evidence is supported by CMIP6 model datasets in section 3.4.VIMT is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence).This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture.The mean anomaly of VIMT (figure S5) exhibits high positive anomalies over the southern tip of the peninsular region, the Gujarat coast, the head Bay and north eastern Indian Ocean region, similar to that of precipitation (figures 3(b) and (d)).Upper level divergence is indicative of lower level convergence, which justifies the moisture flux transport.The IGPs, the foothills of the Himalayas and the Indian Ocean region shows negative VIMT anomalies, supported by the 850 hPa wind anomalies.The increased temperature and precipitation anomalies are indicative of increased GHG concentrations, whereas the decreased temperature and rainfall suggest the cooling effect due to aerosol concentrations.

Surface air temperature (tas) trends from CMIP6
In this section, we evaluate and discuss temporal (figure 4) and spatial (figure 5) surface air temperature trends based on the CMIP6 model for the periods 1951-2000 and 2001-2020 for all three forcings (NAT, AA, and GHG).Trend analysis is performed for normalized annual surface air temperature using a non-parametric Mann-Kendall test with a 95% confidence interval.For temporal trends, the IMD mean temperature is utilised as an observational dataset together with the CMIP6 data.The red colour on the indicator bar denotes natural forcing, the blue colour denotes aerosol forcing, and the green colour denotes GHG forcing.
Figure 4(a) examined the individual temporal trends and errors for the observed temperature for the time periods 1951-2000 and 2001-2020 as well as the model trends for the time period 2000-2020 of NAT, AA, and GHG forcings for each of the models.The figure perfectly demonstrates that the historical GHG forcing is more predominant for temperature trends than the historical aerosol forcing trends.Also, the individual models clearly demonstrate the evidence for GHG forcings dominating (with a trend of 0.02 and above) over aerosol forcing (with a trend of −0.02 and below), for the period 2001-2020, which supports our hypothesis.In accordance with the dominance of GHG forcing, there has been a substantial increase in temperature trends in recent years.The unprecedented warming in the Indian subcontinent triggers the inland moisture availability (through increased water holding capacity  ) than the other forcing, whereas AA predominantly exhibits negative trends.Therefore, it is obvious that GHGs contribute to the temperature increase.Aerosols and GHGs have indeed been observed to exhibit flipping trends.All the models except MRI-ESM2-0 and NorESM2-LM captured (trend > 0) similar spatial trends.However, they showed a positive trend in the case of NAT forcing.As a result, this approach helps us understand how different historical forcing influences surface air   for natural (NAT), anthropogenic aerosols (AA) and greenhouse gases (GHG) forcings derived from the CMIP6 historical dataset.The result is derived with the non-parametric Mann-Kendall test at a 95% confidence level.
temperature.A comparable study for precipitation is described in section 3.5.

Precipitation trends from CMIP6
In this section, similar to the preceding one, we explore temporal and spatial precipitation patterns using the CMIP6 model.The analysis covers the periods 1951-2000 and 2001-2020, encompassing all three historical forcings.The IITM annual rainfall data is used as the observed data along with the CMIP6 precipitation data.GHG forcings for each of the models.After 2000, the observed precipitation data showed a positive trend after initially showing a negative trend.The period 2001-2020 shows that the GHG trend is dominating the aerosol forcing for the models except for MRI-ESM2-0, where the trend due to aerosol forcing is dominating.We can conclude that similar to surface air temperature patterns, historical aerosol forcing trends for precipitation are less significant than historical GHG forcing trends.The comparison of observation and model-based normalized trend plots for precipitation, corresponding to historical all forcing data is shown in the supplementary document.
The spatial trend of precipitation based on the contribution of NAT, AA and GHG forcings from the CMIP6 model for the period 1951-2020 is depicted in figure 6 using a non-parametric Mann-Kendall test with a 95% confidence interval.All eight selected CMIP6 models were analysed similarly and they clearly elucidate the GHG domination over aerosol forcings.In the figure, ACCESS ESM1-5, CanESM5, FGOALS-g3 and IPSL-CM6A-LR are showing positive trend >0.01 mm −1 day −1 yr −1 in the GHG forcing.IPSL-CM6A-LR, MIROC6 and NorESM2-LM are showing higher negative trends less than −0.01 mm −1 day −1 yr −1 , in the case of aerosols.The NAT trends for the ACCESS ESM1-5, FGOALS-g3, and MIROC6 models show a positive spatial trend.GHGs have a major impact on annual precipitation in the Indian subcontinent.The reversal in trend of precipitation in GHGs and aerosols are also evident in figure 6.The signals for surface air temperature are more prominent in comparison to the precipitation trends.

Trends in temperature and precipitation over the monsoon core zone and homogeneous climatic zones
The monsoon core zone (figure S7) bounded by the area 73-82 • E, 18-28 • N is considered the ideal zone (Rajeevan et al 2010) to understand monsoon variations, as the rainfall pattern is uniform in that region.The normalised trend in observed (temperature), model (surface air temperature) and observed and model precipitation for the monsoon core zone is depicted in figures S8(a) and (b).The figure shows the comparison of observation (Obs) and modelbased normalized trend plots for (a) temperature and (b) precipitation  for the monsoon core zone.The observation dataset is taken for two periods, 1951-2000 and 2001-2020 for temperature and precipitation.The model dataset from each of the eight models is taken for both (a) and (b) for the period 2000-2020.For the region, observational data shows an increasing trend (0.052) and positive trend of precipitation in the 2000-2020 period, whereas the precipitation trend is negative for 1951-2000.In the monsoon core zone, the observed temperature trend is decreasing in recent decades when compared to the period of 1951-2000.The model datasets depict the increasing trends of GHG forcings and decreasing trends of AA forcings similar to that of figure 4. In the monsoon core zone, the models exhibit comparable outcomes to those observed over the Indian mainland.The monsoon has obviously revived in the core monsoon zone in the recent two decades, as demonstrated by this study.
The discussion on temperature and precipitation is then extended to homogenous climate zones (figure S6), which are identified by IMD and IITM.Different patterns exist in some locations than in others.Figures S9 and S10 show the regional trends and errors based on the homogeneous climatic zones for both temperature and precipitation, respectively.In figure S9, the temperature datasets for the observational periods 1951-2000 and 2000-2020 are compared to the surface air temperature dataset for the model period 2000-2020.As all other regions except west central show, an increasing trend for temperature for the recent decades, the impact of increasing GHGs is essential to be understood.The dominance of GHG forcings over aerosol and natural forcing is clearly discernible.Model data for the recent 20 years supports substantial rise in observed surface air temperature due to GHG forcing, which is attributable to the dominance of GHGs.The influence of GHG on land warming outweighed the aerosol's influence on land cooling and hence leading to more temperature extremes.Except for the westcentral region (figure S9(b)) observed temperature trend is higher positive (>0.02) in the last two decades.Only for the central northeast (figure S9(d)) the observed temperature trend for the 1951-2000 period is negative.For all the regions aerosol forcing is showing a negative trend (∼−0.02 to −0.06) and GHG is showing a positive trend (0.02-0.04).Similar to temperature, a homogenous region-based precipitation trend analysis is presented and is explained below.
As with the temperature analysis, the precipitation trend analysis represented for the west-central Indian region shows similar results as that of the core monsoon zone (shown in figure S10).In figure S10, the model trends shows that the influence of GHGs is notably predominant over aerosols in the northern and central Indian region.While GHG dominance is observable in other regions, it is not as distinctly discernible.The observational data for the north and west-central regions shows an increasing trend and positive trend of precipitation in the 2000-2020 period, where precipitation trends for both are negative (figures S10(a) and (b)) for 1951-2000.
In figures S10(c)-(e), it is evident that the precipitation trend has been negative and declining in the northeast, central northeast, and peninsular regions over the past two decades.Conversely, the observed data indicates a positive precipitation trend for the northeast and peninsular regions during the 1951-2000 period.The higher aerosol loading in the IGP may be the primary driver of the decreased precipitation trend for observed data in the north region.Absorbing aerosols, such as dust and black carbon, have been shown to potentially enhance the IGP monsoon through heat-pump effects (Lau et al 2006, Jin et al 2021, Wei et al 2022).In the present study, the model trends demonstrate the positive influence of GHG forcings and negative trends of aerosol forcings on precipitation.Even though the flipping trends of GHGs and aerosols are evident, it is noted that the results for temperature are much more distinguishable than those for precipitation.

Conclusions
Over the past few decades, extreme weather events have become more frequent and intense, drawing the scientific community's attention to the appalling face of climate change.From, the beginning of the 21st century, the human community witnessed continuously increasing trends of observed temperature and precipitation, globally as well as regionally.The present study analysed the influence of GHGs and aerosols on the metamorphosis of temperature and precipitation changes over the Indian subcontinent, to understand the comparative dominance and its repercussions.The transition of GHGs to a dominant role over aerosols is acknowledged, yet the precise mechanisms underlying this shift remain elusive.Further investigations are warranted to elucidate the causal factors.GHGs possess longer atmospheric lifetimes relative to aerosols; for instance, CO 2 can persist in the atmosphere for centuries, while aerosols typically endure for shorter periods ranging from days to weeks.This extended longevity enables GHGs to accrue in the atmosphere over time, thereby augmenting their prominence in radiative forcing dynamics.The cooling impact of aerosols is generally deemed to be of lesser magnitude compared to the warming influence exerted by GHGs.Moreover, GHGs exhibit more uniform distribution patterns and wield a more consistent impact on the global climate across extended temporal scales.
Analysis of anomalous regional annual temperature and precipitation variations with respect to the global mean temperature anomaly showed an increasing trend in the recent past from the steady state.Model results are also showing an influence of GHGs (positive trend) and aerosols (negative trend) in temperature and precipitation trends in the recent era.As a result, we concluded that anthropogenic influences have a significant impact on temperature and precipitation variability and consequently contribute to climate change.The spatial anomalies of temperature and precipitation based on the ERA5 data overlaid by 850 hPa wind show the increased temperature and precipitation mean anomalies over certain hotspots, indicating the contribution of GHGs and aerosols on the positive anomalies.Earlier, India was aerosol-dominated (Jin and Wang 2017), recently GHG domination came into existence, which reflects in this unprecedented warming trend.According to CMIP6 model data analysis, aerosol concentrations are growing more negative, especially after 2000, and GHG forcings are increasing with a positive trend.The spatial trend analysis indicates the influence of GHG on surface air temperature increase and the contribution of GHG in rainfall is evident to an extent.The stronger spatial trends are seen for surface air temperature rather than precipitation.As the aerosol trends are decreasing since 2000, it might affect the rainfall pattern over the region.The reduction in aerosol globally will have an impact on regional aerosols and will lead to a reduced surface cooling effect and leads to the warming of the surface, especially as noticed in the Indo-Gangetic region.The spatial anomalies of the temperatures and precipitation show higher positive anomalies in the Indian region for the research timeframe.Aerosols and GHGs influence together to affect temperature and precipitation on a broad scale.The results also indicate that, after the year 2000, GHGs outweighed aerosols, which is indicative of rising global warming and extreme precipitation events.Henceforth, human impact, primarily the effects of GHGs, can be blamed for the enhanced precipitation extremes.Flipping of temperature and precipitation trends over the Indian subcontinent due to diametrically opposing influences of GHGs and aerosols is evident throughout this study.This study contributes to the scientific understanding of the changing climate in the Indian subcontinent, emphasizing the nuanced interactions of GHGs and aerosols and their implications for temperature and precipitation pattern through analysing observed and CMIP6 model datasets.
(Chung and Ramanathan 2006, Roxy et al 2015), (ii) increased emission of anthropogenic aerosols leading to aerosol-induced surface cooling (Ramanathan et al 2005, Bollasina et al 2011), and (iii) land use and land cover change (Paul et al 2016).However, there has been a revival of monsoon since 2002 attributed to increased land warming, as reported by Jin and Wang (2017).Over the last few decades, the CMIPs have been a cornerstone for model development and evaluation (Stevens 2024).Despite significant progress over the past decades, model simulations still exhibit biases and uncertainties due to various factors, such as simplifying assumptions, inaccuracies in model parameterization, boundary conditions, model structure, physical processes, and input variables (Reichler and Kim 2008, Liepert and Previdi 2012).Model Intercomparison projects aim to document and understand these diversities to enhance the credibility and robustness of climate model simulations (Lawrence et al 2016).There are various studies (Stainforth et al 2005, Hawkins and Sutton 2009, 2011, Lehner et al 2020, Beobide-Arsuaga 2021, Yazdandoost et al 2021, John et al 2022, Tett et al 2022) mentioning sources of uncertainty in CMIP6 models.It is to be noted that besides aerosols and GHGs, there are other factors that contribute to the differences in CMIP6 models (Boucher et al 1998, Wang et al 2021, Yu et al 2022).However, the CMIP6 models have undergone updates in their parameterization schemes compared to the CMIP5 models, aiming to better represent the physics and align the climatology of the models with newly available observational datasets (Chen et al 2020, Gusain et al 2020, Bayar et al 2023, Yang and Huang 2023).Natural forcings such as volcanic eruptions and variations in solar radiation can impact climate (Lean and Rind 1998, Hegerl et al 2011), which lead to the involvement of Hist-NAT forcings too in the study.

Figure 1 .
Figure 1.Temperature and precipitation time series for the period 1951-2020 (a) annual temperature (IMD data) variation per year and (b) annual mean rainfall (IITM data) per year.The year 2000 is considered the change point for temperature and precipitation.The slope of the trend values along with p-values are given inside the subplots.
The monthly variation in mean temperature, maximum temperature and precipitation over the Indian subcontinent from 2000-2020 is displayed in figure S4.The peak summer months with the maximum temperatures are April, May, and June (AMJ).Observations of the Indian summer monsoon in Central India have revealed a decreasing rainfall trend in the second half of the 20th century (Ramanathan et al 2005, Bollasina et al 2011, Jin and Wang 2017).In India, more than 80% of rainfall occurs in the peak monsoon months of July, August and September (JAS), as inferred from the IITM annual monthly rainfall data.ISMR onsets around the end of May or around June 1st and enhances further (Ananthakrishnan and Soman 1988, Fasullo and Webster 2003, Joseph et al 2006, Pai and Nair 2009).It advances northwards, usually in surges, and

Figure 2 .
Figure 2. Annual regional temperature and precipitation v/s global mean temperature anomaly based on IMD data (1951-2020, 5 year running mean) (a) mean temperature (b) maximum temperature and (c) minimum temperature and (d) precipitation.

Figure 3 .
Figure 3. 2 m temperature (in K) and precipitation (in mm) mean anomalies overlaid by 850hPa wind anomalies for the period 2001-2020 considering 1959-2000 as the base period for (a) annual temperature (b) annual precipitation (c) temperature (AMJ) and (d) precipitation (JAS).

Figure 4 .
Figure 4. Comparison of observation (Obs) and model-based normalized trend plots for (a) temperature and (b) precipitation (1951-2020).The observation dataset is taken for two periods, 1951-2000 and 2001-2020 for temperature and precipitation.The model dataset from each of the eight models is taken for both (a) and (b) for the period 2000-2020.Multi-model mean (ensemble) is also shown.Here red bars represent natural (NAT), blue bars represent anthropogenic aerosols (AA) and green bars represent greenhouse gases (GHG) forcings from CMIP6 historical dataset.

Figure 5
Figure 5 uses a non-parametric Mann-Kendall test with a 95% confidence interval to indicate the spatial trend of temperature from the contribution of NAT, AA, and GHG forcings from the CMIP6 model over the period 1951-2020.When compared to NAT and AA forcings, GHG trends dominates spatially because it exhibits more positive trends (>0.01 • C yr −1) than the other forcing, whereas AA predominantly exhibits negative trends.Therefore, it is obvious that GHGs contribute to the temperature increase.Aerosols and GHGs have indeed been observed to exhibit flipping trends.All the models except MRI-ESM2-0 and NorESM2-LM captured (trend > 0) similar spatial trends.However, they showed a positive trend in the case of NAT forcing.As a result, this approach helps us understand how different historical forcing influences surface air

Figure 5 .
Figure 5. Spatial representation of the contribution of different forcings to surface air temperature trend patterns over India for natural (NAT), anthropogenic aerosols (AA) and greenhouse gases (GHG) forcings derived from the CMIP6 historical dataset.The result is derived with the non-parametric Mann-Kendall test at a 95% confidence level.
Figure 4(b) demonstrates that similar to the surface air temperature trend, the normalised GHG trend in precipitation is more pronounced than the normalised aerosol trend.The figure analysed the individual temporal trends and errors for the observed precipitation for the time periods 1951-2000 and 2001-2020 as well as the model trends for the time period 2000-2020 of NAT, AA, and

Figure 6 .
Figure 6.Spatial representation of the contribution of different forcing to rainfall trend patterns over India (1951-2020) for natural (NAT), anthropogenic aerosols (AA) and greenhouse gases (GHG) forcings derived from the CMIP6 historical dataset.The result is derived with the non-parametric Mann-Kendall test at a 95% confidence level.

Table 1 .
Observational and reanalysis data specifications.

Table 2 .
List of CMIP6 models considered in this study for the period 1951-2020.

2. Data and methodology
for surface air temperature and precipitation are selected for three historical experiments, i.e. natural only (hist-nat), aerosols only (hist-aer) and greenhouse gases only (hist-GHG), represented as NAT, AA and GHG from here onwards.Each CMIP6 model is developed by different research institutions and organizations, employing various numerical methods, resolutions, and parameterizations (see tables 2 and S1).The selection of the eight CMIP6 models is based on the common availability of surface air temperature and precipitation data for historical NAT, AA and GHG experiments over the necessary research timeframe.To ensure uniformity, a Wang Z, Lin L, Xu Y, Che H, Zhang X, Zhang H, Dong W, Wang C, Gui K and Xie B 2021 Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models npj Clim.Atmos.Sci. 4 2 Warner J and Twomey S 1967 The production of cloud nuclei by cane fires and the effect on cloud droplet concentration J. Atmos.Sci.24 704-6 Wei L, Lu Z, Wang Y, Liu X, Wang W, Wu C, Zhao X, Rahimi S, Xia W and Jiang Y 2022 Black carbon-climate interactions regulate dust burdens over India revealed during COVID-19 ′ Nat.Commun.13 1839 Yang X and Huang P 2023 Improvements in the relationship between tropical precipitation and sea surface temperature from CMIP5 to CMIP6 ′ Clim.Dyn. 60 3319-37 Yazdandoost F, Moradian S, Izadi A and Aghakouchak A 2021 Evaluation of CMIP6 precipitation simulations across different climatic zones: uncertainty and model intercomparison Atmos.Res.250 105369 Yu L, Leng G and Tang Q 2022 Varying contributions of greenhouse gases, aerosols and natural forcings to Arctic land surface air temperature changes Environ.Res.Lett.17 124004