Heightened Adaptability Challenges from Extreme Humid Heat Stress for South Asia

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Monteiro, Pradhiman Bora, Diptiman Bora, Rahul Mahanta, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6273180/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract While the explosive increase in extreme humid heat stress exposure from 2000’s over South Asian monsoon region is challenging human adaptability leading to productivity and mortality loss, factors responsible remain poorly constrained. Here, we unravel that the disruptive regional climate change of decadal-mean maximum Humidex exceeding 45°C to be the primary cause. Over Northeast India, it results in the exposure of extreme heat-stress during the monsoon season rising fourfold to 80 days or 800 ± 278 hours and makes the longest annual extreme moist heat-spell duration increase threefold to 30 days in the 2020’s. The adaptation crisis arises from the average length of spells doubling to 10 days while the average gap between spells decreasing to 3 days. Our findings of changes in characteristics of moist heat spells holds for a large fraction of South Asia, and highlight the urgent need for data on impact of long-spells of continued exposure on human physiology for appropriate advisories and policy interventions. Atmospheric Sciences Humidex Dry bulb temperature Wet bulb temperature Hours of Exposure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Teaser Extreme humid heat stress over South Asia is escalating, with Northeast India facing longer, more frequent heat spells, challenging human adaptability. Introduction The climate action remaining a non-starter, the economic cost arising from productivity and human losses due to extreme weather events are burgeoning( 1 , 2 ). A recent report from International Chamber of Commerce( 3 ) estimates the economic cost from weather extremes during 2014–2023 to be $ 2 Trillion globally. Supporting the same, another study( 4 ) indicates that in low-middle income nations more than 90% of economic damage comes from human loss while it is 60% for high income nations. In the backdrop of rapidly increasing extreme humid heat stress( 5 , 6 ), a large fraction of human loss from weather extremes globally is associated with the increasing survivability and adaptability challenges ( 7 – 9 ). Higher dry bulb temperature and high humidity content in the atmosphere, makes the humid heat stress impact disproportionately high in tropical monsoon regions ( 10 , 11 ). Within the tropics, the Indian sub-continent appears to be a hot-spot in humid heat stress impacts apart from the sub-Saharan Africa (Freychet et al., 2022, Sojan and Srinivasan, 2024, Ivanovich et al., 2024). Within the Indian continent, the north-eastern regions are usually not considered as hotspots for heat stress (whether considering humidity or not), primarily because the heatwave hotspots are considered to be the northwestern, Indus-Ganga plains and coastal southeastern regions ( 9 , 12 , 13 ). However, recent studies (Powis et al., 2023, Freychet et al., 2022) indicate that the Northeast Indian continent (including Bangladesh) is also a hot spot where 2–10 days in a year are expected to exceed the human survivability limit at 1.50C global warming. In this work, we aim to highlight the incidence of heat stress and adaptability challenges over NE Indian continent (a traditionally neglected region) during the monsoon season (a traditionally neglected period) and explore the relevance of our findings to the South Asian region as a whole. We address two outstanding science questions. First, most studies document that the extreme humid heat stress days increases abruptly around 2000’s and explosively thereafter. What regional climate change led to this disruptive change in increase in exposure? Second, how the disruptive change is leading to heightened challenges in adaptability and livability? We argue that a qualitative change in humid heat stress exposure because of this disruptive change in regional climate raises questions on the assumption that the people in the tropics who are acclimatized to a warmer climate are more adaptable to the climate change. We emphasize that a recognition of the limitations of adaptability in the light of the changing character of heat stress exposure in South Asia is imperative for the development of urgently needed adaptation strategy. The impact of heat on health and productivity is considered as one of the most important environmental impacts on humans in current and future climates( 14 ). Globally, South Asia is considered to be one of the hotspots of the impacts of heat due to its monsoonal climate and large and dense population( 9 , 10 , 14 – 20 ). These impacts on health and productivity are due to both increases in temperature with global warming as well as changes in humidity which is at least partly attributable to changes in free-tropospheric moist static energy and convective quasi-equilibrium that prevails across tropical regions( 11 , 21 – 25 ). Despite increasing recognition of the role of humidity in determining the magnitude of heat stress – indeed, nearly every heat stress index accounts for humidity( 26 ) – very few epidemiological studies in South Asia take humidity into account( 27 ), and the operational definition of heatwaves as well as heat action plans are based on temperature alone( 28 – 30 ). Similarly, most studies of heat stress over South Asia focus on the pre-monsoon season, even though high heat stress extends into the monsoon season ( 12 , 13 , 17 , 20 , 31 ). We note that this dichotomy is not unique to South Asia – indeed, the role of humidity in health outcomes is being debated in the health community across the world( 32 ). To consider the joint effects of temperature and humidity, we use Humidex as a heat stress index. We choose Humidex because the behavior of the index is qualitatively consistent with our understanding of human thermophysiological responses( 33 ), which ensures that the qualitative changes in exposure and risk indicated by Humidex is consistent with human thermophysiology even if quantitative recommendations from Humidex may not be strictly applicable. Furthermore, Humidex is easily computable from available meteorological data unlike more sophisticated indices such as Universal Thermal Climate Index (UTCI) and Wet-bulb globe temperature (WBGT)( 34 , 35 ). Recent experimental work on reduction in labor productivity provides calibrated exposure-response curves for Humidex as well( 36 ). Any abrupt change compromises survivability and adaptability. While the cumulative annual exposure to extreme humid heat stress increases by a factor of four between 1940 and 2020, the adaptability is challenged when rate of change abruptly increases by tenfold between 1981 and 2000. The situation is further exacerbated when the process also leads to increasing length of extreme heat spells (continuous exposure). The increasingly longer extreme heat spells with rapidly decreasing gaps between spells (adaptation window) is tantamount to an ‘adaptability crisis’, not recognized so far. Results Disruptive Change of extreme humid heat stress frequency trend The time series of number of days the maximum Humidex that exceeds 45 0 C (which we call “critical heat stress days”) at an urban and a rural location over the Northeast India (Fig. 1 a) during the years between 1940 and 2022 unravel few important facts on humid heat stress increase, that holds true for tropical monsoonal climate in general. The interannual variability of the frequency of extreme Humidex days over the two locations is noted to be highly correlated (r = 0.9) as well as with the ensemble mean frequency over the larger Northeast India (Fig. S1). This observation is indicative of the fact that the extreme humid and dry heat spells over Soth Asia are likely modulated by large-scale meteorological systems like the Monsoon Intraseasonal Oscillations (MISO)( 37 , 38 ). Notably, the frequency of critical heat stress days at the urban location is larger than at a rural station by about 10 days in recent years rising from about 5 days in 1940’s most likely due to a combined effect of higher air temperature and higher humidity. Secondly, this frequency increases slowly from about 10 in 1941 to about 25 in mid-1990’s at the rate of ~ 0.25/year but explosively from 25 in 1995 to 100 in 2022 at the rate of 2.5/year, more than 10 times the rate of increase in the previous period. Our finding at a tropical location is qualitatively similar to Jeong et al. (2023) at an extratropical location but quantitatively an order of magnitude larger. An analysis of the frequency of severe humid heat stress days, with a Humidex exceeding 50°C (Fig. 1 b), reveals that such occurrences were negligible (~ 1 day) until 2000. Since then, the frequency has increased rapidly, reaching about 6 days per year in urban areas and approximately 2 days in rural areas. This discrepancy is further illustrated by the time series of daily maximum Humidex over a 5-year period at both urban and rural locations (Fig. S2). Additionally, the intensity of extreme heat stress events, as measured by the average of the top three daily maximum Humidex observations during the season and across the region, has increased from about 47.3°C in the 1940s to around 49°C in the 2020s (Fig. 1 c). This upward trend underscores the growing severity of humid heat stress over time. By using hourly Humidex data at all locations in the plains in Northeast India, we quantified the total exposure to heat stress exceeding 45 ° C Humidex per year (in hours) from 1940 to 2022 (Fig. 2 ). Like the heat stress days (Fig. 1 a), the exposure increases from about 50 hrs in 1941 to about 200 hrs in 1990 at the rate of 3 hrs/year while increasing to 800 hrs in 2022 at the rate of 15.6 hrs/year (Fig. 2 a). It is notable that the interannual variations of the mean exposure over all locations is in phase with the urban and the rural locations indicates that the variability of exposure is largely governed by the large-scale weather disturbances, as discussed previously. The spread of exposure amongst locations on a given year, however, indicates that the total exposure in a location could be significantly affected by local conditions like soil moisture, vegetation etc. Frequency distributions of exposure during three 10 year periods indicate (Fig. 2 b) that an increase of the maximum yearly exposure from about 600 hours in the early period (1940–1949) to more than 1200 hours in the recent period (2010–2019). The modal yearly exposure shows a dramatic increase from just about 50 hours to more than 600 hours. It is also notable that the frequency distribution changing from an exponential Poisson distribution in early decades to nearly a Gaussian in recent decades. On days when the maximum Humidex exceeds 45 ° C, an examination of frequency of occurrence as a function of hour of the day (Fig. 2 c) shows that exposure to extreme Humidex values occurs during late morning to early evening (consistent with the analysis in Justine et al., 2023)( 12 ). Not only the frequency of peak daily exposure increased by sixfold, the maximum diurnal exposure increased to 20 hours with 12 hour daily exposure being fairly common. This timing suggests that the duration of extreme heat stress could be much longer over the day if solar radiation is accounted for as well. Regional Climate Change responsible for the disruptive change in trend It is natural to expect that with global warming and increasing moisture content in the atmosphere, the ‘expected’ level of humid heat stress (humid heat stress ‘climate’) would be higher leading to increase in frequency of extreme heat stress days. The rate of change of seasonal mean Humidex over the NEI (Fig. S3) around 2000 is only around three and cannot explain the abrupt tenfold increase in the rate extreme heat stress frequency (Fig. 1 ). To try to understand how this comes about, we examine the annual cycles of daily maximum Humidex for one year from a past decade (1940–1949) and a recent decade (2010–2019) along with the corresponding decadal-mean annual cycle of Humidex (Fig. 3 a,b). Consistent with the results of ( 39 ), we find that higher values of Humidex occurs during periods with breaks in rainfall during the rainy season, as seen in Fig. 3 . It may be noted that the daily fluctuations of Humidex and hourly rainfall are weakly inversely related (Fig. 3 ) but the relationship is complex. Heavy small-scale rainfall is immediately followed by local cooling of the surface and lower Humidex while extended cloud cover and light rain associated with synoptic events lower Humidex over a larger region. Very high Humidex values, however, are largely associated with clear sky conditions. The striking difference of the annual cycle of decadal-mean maximum humidex during 1940–1949 and 2010–2019 is the mean changing from a couple of degrees below the critical value (45°C) during the monsoon season of the early period to a couple of degrees above the critical value in the recent decade. This leads to the number of days the Humidex higher than 45°C to exceed only for a few days (~ 20 days) during the early period while in the most recent decade it increased to ~ 100 days. The abrupt increase in this frequency happened when the decadal-mean of maximum Humidex exceeded 45°C during 2000–2009 (Fig. S4). This pattern of increase in the frequency of maximum Humidex exceeding critical value is not biased by selection of sample years chosen in Fig. 3 as could be seen from a similar illustration with another set of sample years (Fig. S4 and Fig. S5). It is implied in Fig. 3 that it is the gradual amplification of the mean annual cycle of air temperature because of global warming and associated nonlinear amplification of mean annual cycle of Humidex is responsible for the increasing trend of the observed frequency of critical Humidex days. To quantify the same, we examine the difference between the mean annual cycle of maximum Humidex and air temperature between the earliest decade (1940–1949) and the latest decade (2010–2019) at the urban location together with the difference between annual cycle daily maximum dry bulb temperature (Fig. 3 c,d). As the humidity contribution to Humidex is a non-linear function of air temperature, the winter with drier atmosphere (e.g. relative humidity 60%) the Humidex does not scale linearly with humidity. The mean annual cycle of air temperature at the urban location (Fig. 3 c) during the summer months has increased by 1.8°C leading the mean Humidex during summer over the urban location to increase by 2.7°C. As the 1.8 0 C change of mean temperature over a period of 80 years is too large compared to the amplitude of multi-decadal variability in temperature (~ 0.2 0 C, Wu et al., 2011) ( 40 ), we attribute the change to the global warming. The process of amplification of air temperature and Humidex is illustrated in Fig. 3 c,d. The rains in North-east India start from April (Fig. 1 of Saha et al., 2023Fig.)( 41 ) and the soil gets saturated leading to a saturation of the maximum surface air temperature from April while the Humidex continues to increase and saturates only from June (Fig. 3 c,d). While by April the climatological air temperature reaches its maximum, climatological surface humidity is far from saturation, continues to increase with the help from winds bringing more moisture from the oceans to the south and ultimately reaching close to saturation in June. Adaptability Challenge and the disruptive regional climate change The decadal-mean maximum Humidex exceeding the critical value (45°C) in the decade 2000–2009 represents a disrupted event or a tipping point opening the possibility of seasonal exposure to increase significantly (Fig. 3 ). A recent study finds that the humid heat stress events over the south Asian monsoon region are modulated by the sub-seasonal oscillations( 37 ). Normally, the frequency of extreme humid stress days would be smaller during humid and rainy active spells while larger during drier break spells of MISO( 42 ). However, when the decadal mean Humidex cross a couple degrees above the climatological mean, a much larger fraction of break spells would tend to remain above critical level thereby substantially increasing the frequency of critical exposure. Therefore, the explosive rate of increase of maximum Humidex exceeding 45°C after the decade 2000–2009 is a natural consequence of the decadal-mean maximum Humidex crossing the critical value (45°C) during the decade 2000–2009 (Fig. S5). As a result, we may expect the longest Length of Spells (LOS) of Humidex exceeding 45 ° C as well as the average length such spells to increase rapidly after 2000–2009. To quantify the same, we examine how the frequency and duration of the extreme Humidex (> 45°C) has changed over time (Fig. 4 ). Not only the frequency of short 3–5 days spells have increased manyfold, the longest spells in a season has also increased significantly during this period (Fig. 4 a). It is notable that, the longest duration of spells has changed from about 15 days during 1940’s to 1990’s and rapidly increased to more than 30 days (right scale in Fig. 4 b) in 2020’s. Also the average LOS has increased from about 5 days in early days to about 10 days in recent years while the average gap between spells has decreased from 40 days to 3 days in recent years (Fig. 4 b). The other notable fact is that the interannual standard deviation of LOS was small about 2 days till about 1990’s and increased threefold to 6 days in recent years (red dashed line Fig. 4 b). This means that longer spells lasting 15–20 days have become more common. The concomitant increase in both the location and scale parameters of the distribution of LOS implies a much rapid increase in extremes as compared to changes in only the mean or standard deviation. In contrast, interannual standard deviation of average gap used to be high (~ 30 days) in early days reducing to ~ 3 days in recent years (blue dashed line, Fig. 4 b). This is essence of the adaptability challenge. Do the extreme moist heat spells also occur at the expense of milder more tolerable heat spells? If so, it would act to further increase the annual moist heat stress burden. To explore this aspect, we examine the frequency of Humidex under different categories. The range of Humidex depicted in Fig. 6 is often divided into 7 categories (Table-1) with increasing level of discomfort and danger from exposure of the humid heat stress. The time series of frequency of occurrence of different categories of Humidex from 1940 to 2023 (Fig. S6) indicates that the frequency of occurrence of category-7 gradually increased from 1940’s while that of category 4 and 5 remain largely unchanged. The increase in frequency of categories-7 in recent decades has happened at the expense of decreasing frequency of 2–3 and 6 categories, suggesting that the changing standard deviation has primarily reflects changes in the positive rather than the negative tail of the distribution of LOS. The warm tropical Indian Ocean together with Western Pacific warm pool (Fig. S7) are the sources of abundant moisture for the Indian summer monsoon weather disturbances. The tropical Indian Ocean is warming at a rate faster than any other world Oceans( 41 , 43 ) and makes the atmosphere over the Indian monsoon region increasingly more unstable and responsible for the increasing trends of extreme daily rainfall events( 44 , 45 ). The Humidex being a nonlinear function of the humidity, we envisage that the increasing trend of the frequency of extreme Humidex days is also strongly related to increasing trend of moisture content because of increasing trend of Indian Ocean SST. To gain some insight into this, we examine the spatial distribution of change in frequency of Humidex between recent decade (2014–2023) and an earlier decade (1940–1949) for three categories, namely category 5, 6 and 7 (Fig. 5 ). It is noted that lower Humidex values corresponding to category 5 or lower occur when the monsoon is in an ‘active’ state with enhanced widespread rainfall and the ITCZ is north of 10 ° N while the higher Humidex values in categories 6 and 7 happen when the Indian monsoon is in a ‘break’ state with widespread decreased rainfall north of 10°N and increased rainfall south of 10°N. This observation is again consistent with some recent studies( 37 – 39 ) and supports our claim that the frequency of the extreme moist heat stress days are modulated by the MISO. The N-S dipole pattern of the change in the frequency is associated with this process. The pattern implies that for the category 5, short but intense weather disturbances within the ‘active’ Inter-tropical Convergence Zone (ITCZ) over the Indian monsoon region in the recent decades lead to decrease in frequency of the lower Humidex values while for category 6 and category 7, more frequent and longer ‘break’ spells on a ‘weaker’ ITCZ over the Indian monsoon region during the recent decades lead to an increase in the frequency of the higher Humidex values. Consistent with the fact that the warm ocean provides the source of moisture, the decrease/increase of Humidex over land are contiguous with decrease/increase of the same over the Ocean. Universality of the Adaptability Crisis To get an insight on whether the adaptability crisis is unique to the Northeast India, we examine the annual exposure over the Indian monsoon region during the recent decade 2011–2019 (Fig. 6 a), its trends (Fig. 6 b), time series of Humidex exceeding 45 ° C at Dhaka and Jaipur (Fig. 6 e) and the annual cycles of decadal-mean maximum Humidex at Dhaka and Jaipur (Fig. 6 c,d). Consistent with other studies (e.g. Saeed et al., 2021, Sojan and Srinivasan, 2024)( 46 , 47 ), high exposure to extreme humid heat stress is experienced not only over the Northeastern region but also along the sub-Himalayan Gangetic plains, northwest India’s semi-arid region and parts of Pakistan. It is also notable that the eastern coast of India experiences exposure to extreme humid heat stress, while the western coast is spared from it. The time series of frequency of days in a year with Humidex exceeding 45 ° C at Dhaka and Jaipur (Fig. 6 e) indicate that the nonlinear trend is like that over Guwahati (Fig. 1 a) with disruptive change in the trend taking place in the decade of 1980–1989 instead of the decade of 1990–1999 in case of Guwahati. Like Guwahati or Dhaka, the rainfall and moisture content along the western coast of India (west of the Western Ghat) are high and yet the exposure to Humidex exceeding 45 ° C along the western coast of India is negligible (Fig. 6 a). The apparent dichotomy is due to the decadal mean Humidex along the western coast of India remaining below 45 ° C even in the latest 2010–2019 decade (Fig. S8, S9). The monsoon winds produce a cooling off the western coast of India during the summer monsoon season, increase primary productivity( 48 ) and keep the mean surface water temperature closer to 27 0 C (Fig. S6, also see Schott et al., 2009)( 49 ) and leads to the mean air temperature during monsoon season colder than pre-monsoon season at around 27.5 0 C at two locations along the western coast (Mumbai and Panaji, Fig. S8,S9). The colder air temperature and associated lower moisture content of the coastal atmosphere led to mean Humidex remaining significantly below the critical level (~ 40 0 C) even during the recent decade! While the annual exposure could vary, the adaptability crisis arising from doubling the average LOS and reducing the gap between spells to about 3 days remains similar throughout the sub-continent. For example, while the humid season in both Guwahati and Dhaka is from May to September, the days Humidex exceeding 45 ° C is 100 for Guwahati and 140 for Dhaka. However, the average LOS has increased from 6 days to 10 days in both Jaipur and Dhaka while the average gap between spells decreased from 35 days to 3 days. In Jaipur, total annual exposure critical heat stress is about 1500 hours in 75 days making average daily exposure of 20 hours! That makes adaptation even more challenging in Jaipur. To illustrate that the adaptability crisis is not limited to Guwahati, Dhaka or Jaipur, we calculated the average LOS during the first decade (1940–1949) and the last decade (2013–2022) (Fig.s 7a,b) together with the average gap between spells during the first and the last decades (Fig.s 7c,d). It may be noted that over the entire region where the frequency of exposure to critical humid heat stress is increasing over Northeast Indian continent including Bangladesh, sub-Himalayan Gangetic plain and northwest Indian semiarid regions, the average LOS is increasing to 30 days while the average gap between spells is decreasing to 3-days in recent years (Fig. 7 a-d). In addition, like the examples shown over Guwahati, Dhaka and Jaipur, the interannual variability LOS has markedly increased and that of the gaps between spells decreased markedly over entire high-exposure regions of South Asia supporting that the adaptability challenge is universal (Fig. 7 e-h)). Discussion Over the past couple of decades, the people living over Northeast Indian continent (including Bangladesh) experienced that the extreme humid heat spells have become longer and more frequent challenging the day-to-day activity during the summer monsoon season. The Northeast India being a key component of the Indian monsoon system and the world’s wettest region, it may be expected. However, neither national nor global studies focused or addressed the challenges in adaptation to exposure to more than 12 hours daily coming in continuous extreme humid heat spells lasting for more than 10 days sometimes lasting as long as 30 days. Up until 1990’s, adaptation to humid heat exposure was feasible due to small total number of days of exposure coming from short-duration spells with much longer gaps between spells for recovery. The situation changed dramatically from 2000’s when the people abruptly faced 3–4 times larger number of days of exposure from much longer-duration spells with much shorter gaps for recovery. The adaptation crisis is not unique to the Northeast Indian sub-continent but generic to whole of South Asia. Unfortunately, recognition of the crisis has been lacking and in the absence of appropriate advisories, outdoor workers in the region are being dangerously exposed and no data available on how it is affecting the health of outdoor workers and contributing to their mortality. Our study quantifies the seriousness of the crisis and compelling evidence presented here provides basis for taking appropriate policy interventions for adaptation and mitigation. We unravel a dramatic increase in the number of days with a Humidex exceeding 45°C in Northeast India, that surged to more than 80 days in the 2020s, a fourfold increase from the roughly 20 days between the 1940s and 1990s. While the rate of increase in frequency of extreme heat-stress days between 1990 and 2020 should be faster than that of the surface temperature increase during the same period (Fig. S3) due to the combined contribution of temperature and humidity( 51 ), but fails to explain the fourfold increase. It also fails to explain the disruptive increase in frequency in 2000’s. The dramatic increase in recent decades is a result of elevating the decadal mean of seasonal mean Humidex to above the critical level of 45°C in 2000’s (Fig. S4g). This results in most summer days without heavy rainfall and during extended periods of dry spells exceed the critical Humidex of 45°C. The cumulative exposure during the summer monsoon season (May-September) has risen to a maximum of about 800 ± 265 hours in recent years, compared to approximately 200 ± 92 hours before the 1990s. More Importantly, longest annual extreme moist heat spell days (LAMD) has increased threefold from 1990’s to more than 30 days in 2020’s from about 10 days in the early period. As a result, the average length of extreme moist heat spells (LOS) has more than doubled from about 5 days in early period to 10 days in recent period. This led to average gap between spells to reduce from more than 10 days to less than 3 days in recent years. The increasing length of the extreme Humidex in recent years are associated with longer duration of ‘break’ spells of monsoon sub-seasonal oscillations and are consistent with expected increasing length of ‘break’ spells( 52 ) because of increasing frequency extreme El Nino events under global warming( 53 ). Our findings of association of the monsoon sub-seasonal oscillations with the increasing trend of extreme humid heat stress over the NEI are in tune with recent finding( 37 ) that humid heat stress events are modulated by the monsoon sub-seasonal oscillations over the whole of Indian monsoon region. Our findings have major implications on how the extreme humid heat spells are impacting health in the tropics. To quantify it better, the studies of association between extreme humid heat stress exposure with respect to longest length of extreme moist heat-stress spell (LLOS) as well as average annual extreme moist heat-stress spell (LOS) in addition to Total Annual Exposure (TAE) are needed. We also find the Weather Advisories in all tropical countries including India are using dry bulb temperature to indicate all extreme heat spells including moist heat spells. For example, a dry bulb temperature of 37°C even when mentioned that it is 5°C above normal does not communicate the seriousness of the situation that at 60% relative humidity, the feel like temperature is 53°C. Therefore, we also argue that weather advisories in tropical countries should prioritize use of heat stress indices such as Humidex rather than solely focusing on dry bulb temperature. Additionally, there is an urgent need for systematic research into the impact of heat stress on morbidity and the progression of heat stress-related illnesses. This research is crucial for developing effective adaptation and mitigation strategies tailored to the tropical context. Materials and Methods Data Availability The primary source of data used in this study is the hourly ERA5 reanalysis data with a horizontal resolution of 0·25° x 0·25°( 54 ). This data includes 2-meter air temperature and 2-meter dew point temperature, from which the Humidex is computed using a nonlinear combination. The reanalysis total temperature is also used to investigate the relationship with Humidex. Additionally, the COBE-SST( 55 ) dataset is utilized to visualize the warming of the Indian tropical ocean. The study focuses on the Northeast part of the South Asian Summer Monsoon region, specifically the plain regions between 89°E to 98°E and 21°N to 30°N. The main study season is from May to September, coinciding with the peak of the summer monsoon​​. To examine the cumulative or mean frequency of heat spells, we excluded hilly and mountainous regions by masking spatial data using Digital Elevation Model (DEM) data with an altitude threshold of 200 meters or less (i.e., grid points within the green contour in the inset map of Fig. 1 c are included only). The DEM data provides an updated global topography and bathymetry grid at a spatial sampling interval of 15 arc seconds( 56 ). For urban and rural grid points, data is selected based on comparative municipal development, specifically choosing Guwahati (25·5°N, 91·5°E; GHY) for the urban grid and Mankachar (25·1°N, 89°E; MKR) for the rural grid. Humidex Calculation Humidex is an index indicating how hot the weather feels to an average person under humid conditions, combining temperature and humidity into one number reflecting perceived temperature. It can be calculated using the formula: $$\:\text{H}\:=\:\text{T}\:+\:0·5555\:\times\:\:\left(\:6·11\:\times\:\:\text{exp}\left(\:\frac{5417·7530\:\times\:\:\left(\:\frac{1}{273·16}\:-\:\frac{1}{273·16\:+\:{\text{T}}_{\text{d}}}\:\right)}{\text{T}}\:\right)\:-\:10\:\right)$$ where, T: The air temperature in degrees Celsius (°C). T d : The dew point temperature in degrees Celsius (°C). The dew point is the temperature at which air becomes saturated with moisture and water vapor starts to condense. 0·5555: A constant used in the formula to scale the effect of the dew point temperature on the Humidex value. 6·11: This is the vapor pressure of water in hectopascals (hPa) at the temperature of 0°C, a constant used in calculating the saturated vapor pressure. exp: The exponential function, which raises the mathematical constant e (approximately equal to 2·71828) to the power of the expression within the parentheses. It is used here to model the relationship between temperature and vapor pressure. 5417·7530: This is a constant that comes from the Clausius–Clapeyron relation, which describes the phase transition between two states of matter, in this case, water vapor and liquid water. It represents the energy required to change water from liquid to gas. 273·16: These are constants representing the temperature in Kelvin for absolute zero plus 0.01 degrees Celsius and 0 degrees Celsius, respectively. This conversion is necessary because the exponential term in the equation involves temperatures in Kelvin. 10: A constant subtracted to normalize the equation to typical conditions. The relationship between Humidex and the appropriate response for outdoor activity is detailed in the Occupational Health Clinics for Ontario Workers (OHCOW)( 57 ) Humidex based heat response plan, as shown in Table 1 . Table 1 Humidex-Based Heat Stress Guidelines and Recommended Preventive Measures for Workers. This table represents heat stress guidelines based on the Humidex index, outlining risk categories and recommended preventive measures for workers to ensure safety during hot weather conditions. Category Adjusted * Humidex (°C) Effective ** Humidex (°C) Guidelines Category-1 25 to 29 ≤ 23·0 Supply water to workers on an "as needed" basis Category-2 30 to 33 23.1 to 24.0 Post Heat Stress Warning notice; encourage workers to drink extra water; start recording hourly temperature and relative humidity Category-3 34 to 37 24.1 to 25.0 Post Heat Stress Warning notice; notify workers that they need to drink extra water; ensure workers are trained to recognize symptoms Category-4 38 to 39 25.1 to 26.0 Work with 15 minutes relief per hour; provide adequate cool water (10–15°C); at least 1 cup (240mL) of water every 20 minutes; workers with symptoms should seek medical attention Category-5 40 to 41 26.1 to 27.0 Work with 30 minutes relief per hour; continue in addition to the provisions listed previously Category-6 42 to 44 27.1 to 29.0 If feasible, work with 45 minutes relief per hour; continue in addition to the provisions listed above Category-7 ≥ 45 ≥ 29.1 Only medically supervised work can continue *Adjusted means adjusted for additional clothing and radiant heat **Effective means adjusted for clothing At air temperature of 30 ° C and relative humidity (RH) 70%, it feels like 42 ° C and at 80% RH it feels like 45 ° C while at air temperature of 35 ° C and RH 60% it feels like 50 0 C and at 70% RH it feels like 55 ° C (Fig. S10). Over the monsoonal regions of the tropics like the Northeast India, air temperature of 35 ° C to 38 ° C with 70% − 80% RH is common during May to September. Data Analysis The maximum, minimum, and mean Humidex values are computed from hourly data from 1940–2023. The study region is primarily Northeast India, but it also includes glimpses of heat spell frequency over the broader South Asian Summer Monsoon domain. To study the cumulative or mean frequency of heat spells over all grid points in Northeast India, the data from areas with altitudes below 200 meters is extracted. Different urban and rural differences are examined by selecting specific grid points representing Guwahati and Mankachar. The analysis includes visualizing the time series of the number of days the maximum Humidex exceeds 45°C at both urban and rural locations. Furthermore, the relationship between Humidex and precipitation is analyzed to understand the meteorological controls on extreme heat stress frequency. Statistical Analysis To analyse trends in the frequency of critical humid heat stress days, we applied a continuous piecewise linear regression approach to the time series data, dividing it into two distinct segments. This method enabled us to quantify shifts in the rate of increase across different periods, revealing changes in heat stress trends over time. A continuous piecewise linear function includes breakpoints that signify the transition points between line segments, allowing us to model a slower trend in the earlier period and a more accelerated trend in recent decades. Since the exact location of the breakpoint was unknown, we employed a global optimization technique, differential evolution, to identify the optimal year. This technique was selected for its ability to explore a wide range of potential breakpoints and minimize fitting error. Specifically, it iteratively tested different breakpoint positions, applying least squares fitting to identify the location that best represented the natural transition in trends with minimal error( 50 ). Spatial changes in the frequency of Humidex values are examined by comparing the means from present decades (2014–2023) to past decades (1940–1949) over the South Asian Summer Monsoon (SASM) region. This helps to understand the broader spatial patterns and regional variations in humid heat stress. Declarations Acknowledgments P.S. and R.M. extend their appreciation to Cotton University for providing the essential infrastructure and resources necessary for conducting this research. BNG thanks Science and Engineering Research Board (SERB), Government of India for supporting the computational facility for this work and Gauhati University for the Honorary Professor of Excellence. Author contributions: Conceptualization: PS, BNG Methodology: PS, BNG, JMM Investigation: PS Data Analysis (Major): PS Data Analysis (Partial): DB, PB Visualization: PS Supervision: BNG Writing—original draft: BNG Writing—review & editing: JMM, RM, PS, BNG Competing interests: The authors declare that they have no competing interests. Data and materials availability: The datasets utilized in this study are publicly accessible from reputable sources. The ERA5 data were obtained from the Complete ERA5 Global Atmospheric Reanalysis provided by the Copernicus Climate Data Store. Global bathymetry data from SRTM15+ were sourced from the Scripps Institution of Oceanography. Additionally, COBE monthly SST data were retrieved from NOAA's Physical Sciences Laboratory. All computations and analyses for this study were conducted using open-source software tools, ensuring transparency and reproducibility. These include the Climate Data Operators (CDO), available at (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-complete?tab=overview, and the NCAR Command Language (NCL), developed by the Computational & Information Systems Laboratory at the National Center for Atmospheric Research (NCAR) and sponsored by the National Science Foundation (https://www.ncl.ucar.edu/). Additionally, Python, an open source programming language widely used for scientific data processing and visualization, was employed, and its resources can be accessed at https://www.python.org/downloads/. References Waidelich P, Batibeniz F, Rising J, Kikstra JS, Seneviratne SI (2024) Climate damage projections beyond annual temperature. Nat Clim Chang 14:592–599 Kotz M, Levermann A, Wenz L (2024) The economic commitment of climate change. 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J Clim 27 Tozer B, Sandwell DT, Smith WHF, Olson C, Beale JR, Wessel P (2019) Global Bathymetry and Topography at 15 Arc Sec: SRTM15+. Earth Sp Sci 6:1847–1864 C. C. for Occupational Health, Safety, Humidex Rating and Work. (2024) Additional Declarations The authors declare no competing interests. Supplementary Files 21MARCH2025OnlineSupplementaryMaterialresearchsquare.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6273180","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431844650,"identity":"b0f1612e-41f3-499b-bb19-95f4039ef978","order_by":0,"name":"Prolay Saha","email":"","orcid":"https://orcid.org/0000-0002-9404-9029","institution":"Cotton University, India","correspondingAuthor":false,"prefix":"","firstName":"Prolay","middleName":"","lastName":"Saha","suffix":""},{"id":431844651,"identity":"7c6c0f1b-bacb-424d-8a2e-54a45b73938a","order_by":1,"name":"Joy M. Monteiro","email":"","orcid":"","institution":"Indian Institute of Science Education and Research Pune, Pune, India","correspondingAuthor":false,"prefix":"","firstName":"Joy","middleName":"M.","lastName":"Monteiro","suffix":""},{"id":431844652,"identity":"3382c455-662b-4ebe-99c7-875a1d160f21","order_by":2,"name":"Pradhiman Bora","email":"","orcid":"","institution":"University of Arizona, USA","correspondingAuthor":false,"prefix":"","firstName":"Pradhiman","middleName":"","lastName":"Bora","suffix":""},{"id":431844653,"identity":"68191060-d40d-44e4-9d2c-b994d012f228","order_by":3,"name":"Diptiman Bora","email":"","orcid":"","institution":"University of Arizona, USA","correspondingAuthor":false,"prefix":"","firstName":"Diptiman","middleName":"","lastName":"Bora","suffix":""},{"id":431844654,"identity":"8d4ebf45-9e42-4e9d-9d7d-7d9c6d97e0ad","order_by":4,"name":"Rahul Mahanta","email":"","orcid":"","institution":"Cotton University, India","correspondingAuthor":false,"prefix":"","firstName":"Rahul","middleName":"","lastName":"Mahanta","suffix":""},{"id":431844655,"identity":"06f95e27-8aee-465d-9d23-ae523f69868e","order_by":5,"name":"B. 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Goswami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACAwlkXoKBBA8/mFFAtJYCGznJBrBeYrUwfEgzNjgAFsetxVy6x+zBxx21cvIzkp9ueGBwOHHz+dWJHx4YMMjzix3AqsVyzhlzw5lnjhsb3Egzu5EA1LLtxtvNEkCHGc6cnYDdYTdyzKR5244lbpBOgGk5uwGkJcHgNh4tf4Fa5s9O/wbWsnnG2c0/CGphbKtJbLidA7IF6H3+3m14bbGckVZu2Nt2wNjg/psyoBYbOYkbvNssgBGE0y/mEsnbHvxsq5OT7zm+7eaPP8Co7D+7+eaPCht5fmnsWoCADYgPI/ElwColsKpF0lKHxOc/gE/1KBgFo2AUjEAAACGnaeIjgcfwAAAAAElFTkSuQmCC","orcid":"","institution":"Cotton University, India","correspondingAuthor":true,"prefix":"","firstName":"B.","middleName":"N.","lastName":"Goswami","suffix":""}],"badges":[],"createdAt":"2025-03-21 01:57:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6273180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6273180/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79152206,"identity":"10e3fe81-44c4-49de-a75d-0d74036ac672","added_by":"auto","created_at":"2025-03-25 05:03:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":689674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNonlinear trends of frequency and intensity of Extreme Humidex days.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eTime series of the annual number of days the maximum Humidex exceeds 450C at Guwahati ( GHY, red) and Mankachar (MKR, blue). \u003cstrong\u003eb\u003c/strong\u003e Similar to panel (a), but for the frequency of days when the maximum Humidex exceeds 50°C for the same urban (red) and rural (blue) locations. \u003cstrong\u003ec \u003c/strong\u003eThe time series of the ensemble mean intensity of maximum Humidex 45°C over all Northeast (NE) India locations. The dashed lines represent the best-linear fit trends for the periods 1940-1999 and 2000-2023. The inset map highlights the study region NE with topographic height ≤ 200 m. The correlation coefficient between interannual variations of frequency at GHY and MKR (Fig. 1a (b)) is 0.9 (0.6).\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6273180/v1/b753ffb40bf4ca56d4113258.png"},{"id":79152207,"identity":"37edb765-fa5a-4648-b32a-3aac4c3d5db0","added_by":"auto","created_at":"2025-03-25 05:03:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3003887,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Exposure (hours) to Extreme Heat-stress Days over NE.\u003c/p\u003e\n\u003cp\u003ea The annual hours of exposure to extreme heat (humidex exceeding 45°C) from 1940 to 2020 across Northeast India. Gray lines represent data from individual locations, with the thick black line showing the ensemble mean and its 95% confidence interval shaded in light grey. The red and blue lines indicate GHY and MKR areas, respectively. The green shaded area represents the standard deviation of spread across locations. b Frequency distribution of hours of exposure for three periods, past (1940-1949, in blue), middle (1970-1979, in green) and Present (2010-2019, in red). The past period shows a more concentrated distribution with fewer hours of exposure, resembling a theoretical Gamma distribution. In contrast, the present period displays a broader, Gaussian-like distribution, with more frequent extreme heat exposure.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6273180/v1/50a9ce1e3e2d8526cb84e0c1.png"},{"id":79152210,"identity":"d92c0032-58c1-42b5-b06e-70ea74115227","added_by":"auto","created_at":"2025-03-25 05:03:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":976068,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Cycle of Maximum Humidex and Precipitation.\u003c/p\u003e\n\u003cp\u003eThis figure presents the annual cycle of the daily maximum Humidex (red, left y-axis) and daily total precipitation (green, right y-axis) at GHY for the following periods: a The year 1941, along with the climatology of maximum Humidex and daily total precipitation from 1940-1949. b The year 2011 along with daily total precipitation together with climatology of maximum Humidex from 2010-2019. c The difference (shaded red) between the mean annual cycle of maximum Humidex for the past decade (1940-1949) and the present decade (2010-2019), along with the difference in the annual cycle of daily maximum air temperature (pink) at an urban location. d The progression of the climatology of daily maximum Humidex across all eight decades at the urban location. The dotted horizontal line denotes 45 °C.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6273180/v1/ffa79c929776dd2a5c2ad23c.png"},{"id":79152208,"identity":"2c006141-2669-4a9a-a419-84e997717e24","added_by":"auto","created_at":"2025-03-25 05:03:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":552482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvolution of 2D Frequency distribution in Heatwave durations over Northeast India.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eA 2D histogram of heatwave event frequency over Northeast India, with the x-axis representing years (1940 to 2023) and the y-axis indicating the duration of persistent heatwave\u003c/p\u003e\n\u003cp\u003eevents in days (e.g., 3-day, 5-day events, up to 40 days). The color bar reflects the average\u003c/p\u003e\n\u003cp\u003ecount of heatwave events per grid, calculated by dividing the total event count by the number\u003c/p\u003e\n\u003cp\u003eof grids in the region (390). \u003cstrong\u003eb\u003c/strong\u003e Time series of average Length of Spells and its standard deviation with dashed curve (LOS, Left Y-axis in red), average length of gap days between the spells and its standard deviation with dashed curve ( GAP, Right Y-axis in blue), and average length of longest Spell days (Max. LOS Right Y-axis in green) from 1940 to 2023 for MJJAS season, ensembles mean over all grid points (as shown in inset Fig1c) over NE. The dashed curves representing the standard deviation are smoothed using a 7-year running mean.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6273180/v1/e03e35779b9993244e1f613c.png"},{"id":79152900,"identity":"8a62f398-78fa-49f7-b5ed-5219855e187c","added_by":"auto","created_at":"2025-03-25 05:11:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1618146,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Change in Frequency of Maximum Humidex Across South Asian Summer Monsoon Region.\u003c/p\u003e\n\u003cp\u003eThis figure shows the spatial change in the frequency of maximum Humidex (calculated as the mean difference between 2014-2023 and 1940-1949) over the South Asian Summer Monsoon region for the months of May (a, f, k), June (b, g, l), July (c, h, m), August (d, i, n), and September (e, j, o), respectively. The color scale represents the change in days per year for three different categories of Humidex. The unit of the color bar is days/year for three categories of Humidex.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6273180/v1/71e18c7b5466d727e4e32ad7.png"},{"id":79152214,"identity":"038b8dbf-419b-420d-b283-bba224f4e5de","added_by":"auto","created_at":"2025-03-25 05:03:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1194159,"visible":true,"origin":"","legend":"\u003cp\u003eHumidex Exposure and Trends in South Asia.\u003c/p\u003e\n\u003cp\u003ea Decadal (2010-2019) average of hours of exposure of Humidex greater than 45 °C at each grid points over South Asian landmass. b Long term (1940-2023) trends of hours of exposure of Humidex greater than 45 °C at each grid points over South Asian landmass. c Annual cycle of maximum humidex for three decades, viz., past (1940-1949, in blue), middle (1970-1979, in green) and Present (2010-2019, in red) at Jaipur. d Same as (c) but at Dhaka. e Time series of the annual number of days the maximum Humidex exceeds 45\u003csup\u003e°\u003c/sup\u003eC at Jaipur (red) and Dhaka (blue), and the dashed (both red and blue) lines represent the best-linear fit trends for two distinct range of periods(\u003cem\u003e50\u003c/em\u003e). f Time series of average Length of Spells (LOS, Left Y-axis in pink) and average length of gap days between the spells (GAP, Right Y-axis in green) from 1940 to 2023 for MJJAS season at Jaipur (solid curves) and at Dhaka (dashed curves), and smoothed (both sloid and dashed) curves are the second order polynomial fit curves.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6273180/v1/8e6fe49e9b6d4bca8046c45c.png"},{"id":79152902,"identity":"e138c29e-d1aa-4be7-902e-6c66d9e6a3cc","added_by":"auto","created_at":"2025-03-25 05:11:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1288439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 7. Contrast of LOS and Gap between Past and Present decades.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eAverage length of Spells (LOS) based on Humidex \u0026gt; 45°C between 1940-1949. \u003cstrong\u003eb\u003c/strong\u003eAverage length of Spells (LOS) based on Humidex \u0026gt; 45°C between 2010-2019. \u003cstrong\u003ec\u003c/strong\u003eAverage length of Gap between the spells based on Humidex \u0026gt; 45°C between 1940-1949. \u003cstrong\u003ed\u003c/strong\u003e Average length of Gap between the spells based on Humidex \u0026gt; 45°C between 2010-2019. \u003cstrong\u003ee\u003c/strong\u003e Average Standard deviation (SD) of length of Spells (LOS) based on Humidex \u0026gt; 45°C between 1940-1949. \u003cstrong\u003ef\u003c/strong\u003eAverage Standard deviation (SD) of length of Spells (LOS) based on Humidex \u0026gt; 45°C between 2010-2019. \u003cstrong\u003eg\u003c/strong\u003e Average Standard deviation (SD) of length of Gap between the spells based on Humidex \u0026gt; 45°C between 1940-1949. \u003cstrong\u003eh\u003c/strong\u003eAverage Standard deviation (SD) of length of Gap between the spells based on Humidex \u0026gt; 45°C between 2010-2019.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6273180/v1/3c3596f8ba5d99e600667cb9.png"},{"id":79155185,"identity":"c86a8be8-85cd-48c6-9c89-dbd25ab47dd9","added_by":"auto","created_at":"2025-03-25 06:00:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9376586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6273180/v1/e0fe6c65-6c23-4427-8c58-25984e113a69.pdf"},{"id":79152899,"identity":"24929067-61e5-4c31-a8ee-1b474448087d","added_by":"auto","created_at":"2025-03-25 05:11:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2169675,"visible":true,"origin":"","legend":"","description":"","filename":"21MARCH2025OnlineSupplementaryMaterialresearchsquare.docx","url":"https://assets-eu.researchsquare.com/files/rs-6273180/v1/e4d0f1a7e630a07aa370c2d5.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eHeightened Adaptability Challenges from Extreme Humid Heat Stress for South Asia\u003c/p\u003e","fulltext":[{"header":"Teaser","content":"\u003cp\u003eExtreme humid heat stress over South Asia is escalating, with Northeast India facing longer, more frequent heat spells, challenging human adaptability.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe climate action remaining a non-starter, the economic cost arising from productivity and human losses due to extreme weather events are burgeoning(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). A recent report from International Chamber of Commerce(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) estimates the economic cost from weather extremes during 2014\u0026ndash;2023 to be \u003cspan\u003e$\u003c/span\u003e2 Trillion globally. Supporting the same, another study(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) indicates that in low-middle income nations more than 90% of economic damage comes from human loss while it is 60% for high income nations. In the backdrop of rapidly increasing extreme humid heat stress(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), a large fraction of human loss from weather extremes globally is associated with the increasing survivability and adaptability challenges (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Higher dry bulb temperature and high humidity content in the atmosphere, makes the humid heat stress impact disproportionately high in tropical monsoon regions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Within the tropics, the Indian sub-continent appears to be a hot-spot in humid heat stress impacts apart from the sub-Saharan Africa (Freychet et al., 2022, Sojan and Srinivasan, 2024, Ivanovich et al., 2024). Within the Indian continent, the north-eastern regions are usually not considered as hotspots for heat stress (whether considering humidity or not), primarily because the heatwave hotspots are considered to be the northwestern, Indus-Ganga plains and coastal southeastern regions (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, recent studies (Powis et al., 2023, Freychet et al., 2022) indicate that the Northeast Indian continent (including Bangladesh) is also a hot spot where 2\u0026ndash;10 days in a year are expected to exceed the human survivability limit at 1.50C global warming. In this work, we aim to highlight the incidence of heat stress and adaptability challenges over NE Indian continent (a traditionally neglected region) during the monsoon season (a traditionally neglected period) and explore the relevance of our findings to the South Asian region as a whole. We address two outstanding science questions. First, most studies document that the extreme humid heat stress days increases abruptly around 2000\u0026rsquo;s and explosively thereafter. What regional climate change led to this disruptive change in increase in exposure? Second, how the disruptive change is leading to heightened challenges in adaptability and livability? We argue that a qualitative change in humid heat stress exposure because of this disruptive change in regional climate raises questions on the assumption that the people in the tropics who are acclimatized to a warmer climate are more adaptable to the climate change. We emphasize that a recognition of the limitations of adaptability in the light of the changing character of heat stress exposure in South Asia is imperative for the development of urgently needed adaptation strategy. The impact of heat on health and productivity is considered as one of the most important environmental impacts on humans in current and future climates(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Globally, South Asia is considered to be one of the hotspots of the impacts of heat due to its monsoonal climate and large and dense population(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These impacts on health and productivity are due to both increases in temperature with global warming as well as changes in humidity which is at least partly attributable to changes in free-tropospheric moist static energy and convective quasi-equilibrium that prevails across tropical regions(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Despite increasing recognition of the role of humidity in determining the magnitude of heat stress \u0026ndash; indeed, nearly every heat stress index accounts for humidity(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) \u0026ndash; very few epidemiological studies in South Asia take humidity into account(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), and the operational definition of heatwaves as well as heat action plans are based on temperature alone(\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Similarly, most studies of heat stress over South Asia focus on the pre-monsoon season, even though high heat stress extends into the monsoon season (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). We note that this dichotomy is not unique to South Asia \u0026ndash; indeed, the role of humidity in health outcomes is being debated in the health community across the world(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). To consider the joint effects of temperature and humidity, we use Humidex as a heat stress index. We choose Humidex because the behavior of the index is qualitatively consistent with our understanding of human thermophysiological responses(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), which ensures that the qualitative changes in exposure and risk indicated by Humidex is consistent with human thermophysiology even if quantitative recommendations from Humidex may not be strictly applicable. Furthermore, Humidex is easily computable from available meteorological data unlike more sophisticated indices such as Universal Thermal Climate Index (UTCI) and Wet-bulb globe temperature (WBGT)(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Recent experimental work on reduction in labor productivity provides calibrated exposure-response curves for Humidex as well(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Any abrupt change compromises survivability and adaptability. While the cumulative annual exposure to extreme humid heat stress increases by a factor of four between 1940 and 2020, the adaptability is challenged when rate of change abruptly increases by tenfold between 1981 and 2000. The situation is further exacerbated when the process also leads to increasing length of extreme heat spells (continuous exposure). The increasingly longer extreme heat spells with rapidly decreasing gaps between spells (adaptation window) is tantamount to an \u0026lsquo;adaptability crisis\u0026rsquo;, not recognized so far.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eDisruptive Change of extreme humid heat stress frequency trend\u003c/h2\u003e\n \u003cp\u003eThe time series of number of days the maximum Humidex that exceeds 45\u003csup\u003e0\u003c/sup\u003eC (which we call \u0026ldquo;critical heat stress days\u0026rdquo;) at an urban and a rural location over the Northeast India (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea) during the years between 1940 and 2022 unravel few important facts on humid heat stress increase, that holds true for tropical monsoonal climate in general. The interannual variability of the frequency of extreme Humidex days over the two locations is noted to be highly correlated (r\u0026thinsp;=\u0026thinsp;0.9) as well as with the ensemble mean frequency over the larger Northeast India (Fig. S1). This observation is indicative of the fact that the extreme humid and dry heat spells over Soth Asia are likely modulated by large-scale meteorological systems like the Monsoon Intraseasonal Oscillations (MISO)(\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e). Notably, the frequency of critical heat stress days at the urban location is larger than at a rural station by about 10 days in recent years rising from about 5 days in 1940\u0026rsquo;s most likely due to a combined effect of higher air temperature and higher humidity. Secondly, this frequency increases slowly from about 10 in 1941 to about 25 in mid-1990\u0026rsquo;s at the rate of ~\u0026thinsp;0.25/year but explosively from 25 in 1995 to 100 in 2022 at the rate of 2.5/year, more than 10 times the rate of increase in the previous period. Our finding at a tropical location is qualitatively similar to Jeong et al. (2023) at an extratropical location but quantitatively an order of magnitude larger.\u003c/p\u003e\n \u003cp\u003eAn analysis of the frequency of severe humid heat stress days, with a Humidex exceeding 50\u0026deg;C (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb), reveals that such occurrences were negligible (~\u0026thinsp;1 day) until 2000. Since then, the frequency has increased rapidly, reaching about 6 days per year in urban areas and approximately 2 days in rural areas. This discrepancy is further illustrated by the time series of daily maximum Humidex over a 5-year period at both urban and rural locations (Fig. S2). Additionally, the intensity of extreme heat stress events, as measured by the average of the top three daily maximum Humidex observations during the season and across the region, has increased from about 47.3\u0026deg;C in the 1940s to around 49\u0026deg;C in the 2020s (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec). This upward trend underscores the growing severity of humid heat stress over time.\u003c/p\u003e\n \u003cp\u003eBy using hourly Humidex data at all locations in the plains in Northeast India, we quantified the total exposure to heat stress exceeding 45\u003csup\u003e\u003cstrong\u003e\u0026deg;\u003c/strong\u003e\u003c/sup\u003eC Humidex per year (in hours) from 1940 to 2022 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Like the heat stress days (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea), the exposure increases from about 50 hrs in 1941 to about 200 hrs in 1990 at the rate of 3 hrs/year while increasing to 800 hrs in 2022 at the rate of 15.6 hrs/year (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). It is notable that the interannual variations of the mean exposure over all locations is in phase with the urban and the rural locations indicates that the variability of exposure is largely governed by the large-scale weather disturbances, as discussed previously. The spread of exposure amongst locations on a given year, however, indicates that the total exposure in a location could be significantly affected by local conditions like soil moisture, vegetation etc. Frequency distributions of exposure during three 10 year periods indicate (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb) that an increase of the maximum yearly exposure from about 600 hours in the early period (1940\u0026ndash;1949) to more than 1200 hours in the recent period (2010\u0026ndash;2019). The modal yearly exposure shows a dramatic increase from just about 50 hours to more than 600 hours. It is also notable that the frequency distribution changing from an exponential Poisson distribution in early decades to nearly a Gaussian in recent decades. On days when the maximum Humidex exceeds 45\u003csup\u003e\u003cstrong\u003e\u0026deg;\u003c/strong\u003e\u003c/sup\u003eC, an examination of frequency of occurrence as a function of hour of the day (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec) shows that exposure to extreme Humidex values occurs during late morning to early evening (consistent with the analysis in Justine et al., 2023)(\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e). Not only the frequency of peak daily exposure increased by sixfold, the maximum diurnal exposure increased to 20 hours with 12 hour daily exposure being fairly common. This timing suggests that the duration of extreme heat stress could be much longer over the day if solar radiation is accounted for as well.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eRegional Climate Change responsible for the disruptive change in trend\u003c/h3\u003e\n\u003cp\u003eIt is natural to expect that with global warming and increasing moisture content in the atmosphere, the \u0026lsquo;expected\u0026rsquo; level of humid heat stress (humid heat stress \u0026lsquo;climate\u0026rsquo;) would be higher leading to increase in frequency of extreme heat stress days. The rate of change of seasonal mean Humidex over the NEI (Fig. S3) around 2000 is only around three and cannot explain the abrupt tenfold increase in the rate extreme heat stress frequency (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). To try to understand how this comes about, we examine the annual cycles of daily maximum Humidex for one year from a past decade (1940\u0026ndash;1949) and a recent decade (2010\u0026ndash;2019) along with the corresponding decadal-mean annual cycle of Humidex (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea,b). Consistent with the results of (\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e), we find that higher values of Humidex occurs during periods with breaks in rainfall during the rainy season, as seen in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eIt may be noted that the daily fluctuations of Humidex and hourly rainfall are weakly inversely related (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) but the relationship is complex. Heavy small-scale rainfall is immediately followed by local cooling of the surface and lower Humidex while extended cloud cover and light rain associated with synoptic events lower Humidex over a larger region. Very high Humidex values, however, are largely associated with clear sky conditions. The striking difference of the annual cycle of decadal-mean maximum humidex during 1940\u0026ndash;1949 and 2010\u0026ndash;2019 is the mean changing from a couple of degrees below the critical value (45\u0026deg;C) during the monsoon season of the early period to a couple of degrees above the critical value in the recent decade. This leads to the number of days the Humidex higher than 45\u0026deg;C to exceed only for a few days (~\u0026thinsp;20 days) during the early period while in the most recent decade it increased to ~\u0026thinsp;100 days. The abrupt increase in this frequency happened when the decadal-mean of maximum Humidex exceeded 45\u0026deg;C during 2000\u0026ndash;2009 (Fig. S4). This pattern of increase in the frequency of maximum Humidex exceeding critical value is not biased by selection of sample years chosen in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e as could be seen from a similar illustration with another set of sample years (Fig. S4 and Fig. S5).\u003c/p\u003e\n\u003cp\u003eIt is implied in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e that it is the gradual amplification of the mean annual cycle of air temperature because of global warming and associated nonlinear amplification of mean annual cycle of Humidex is responsible for the increasing trend of the observed frequency of critical Humidex days. To quantify the same, we examine the difference between the mean annual cycle of maximum Humidex and air temperature between the earliest decade (1940\u0026ndash;1949) and the latest decade (2010\u0026ndash;2019) at the urban location together with the difference between annual cycle daily maximum dry bulb temperature (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec,d). As the humidity contribution to Humidex is a non-linear function of air temperature, the winter with drier atmosphere (e.g. relative humidity\u0026thinsp;\u0026lt;\u0026thinsp;30%) changes in Humidex corresponds closely to changes in the air temperature while during the summer with higher moisture content in the atmosphere (e.g. relative humidity\u0026thinsp;\u0026gt;\u0026thinsp;60%) the Humidex does not scale linearly with humidity. The mean annual cycle of air temperature at the urban location (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec) during the summer months has increased by 1.8\u0026deg;C leading the mean Humidex during summer over the urban location to increase by 2.7\u0026deg;C. As the 1.8\u003csup\u003e0\u003c/sup\u003eC change of mean temperature over a period of 80 years is too large compared to the amplitude of multi-decadal variability in temperature (~\u0026thinsp;0.2\u003csup\u003e0\u003c/sup\u003eC, Wu et al., 2011) (\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e), we attribute the change to the global warming. The process of amplification of air temperature and Humidex is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec,d. The rains in North-east India start from April (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e of Saha et al., 2023Fig.)(\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e) and the soil gets saturated leading to a saturation of the maximum surface air temperature from April while the Humidex continues to increase and saturates only from June (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec,d). While by April the climatological air temperature reaches its maximum, climatological surface humidity is far from saturation, continues to increase with the help from winds bringing more moisture from the oceans to the south and ultimately reaching close to saturation in June.\u003c/p\u003e\n\u003ch3\u003eAdaptability Challenge and the disruptive regional climate change\u003c/h3\u003e\n\u003cp\u003eThe decadal-mean maximum Humidex exceeding the critical value (45\u0026deg;C) in the decade 2000\u0026ndash;2009 represents a disrupted event or a tipping point opening the possibility of seasonal exposure to increase significantly (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). A recent study finds that the humid heat stress events over the south Asian monsoon region are modulated by the sub-seasonal oscillations(\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e). Normally, the frequency of extreme humid stress days would be smaller during humid and rainy active spells while larger during drier break spells of MISO(\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e). However, when the decadal mean Humidex cross a couple degrees above the climatological mean, a much larger fraction of break spells would tend to remain above critical level thereby substantially increasing the frequency of critical exposure. Therefore, the explosive rate of increase of maximum Humidex exceeding 45\u0026deg;C after the decade 2000\u0026ndash;2009 is a natural consequence of the decadal-mean maximum Humidex crossing the critical value (45\u0026deg;C) during the decade 2000\u0026ndash;2009 (Fig. S5).\u003c/p\u003e\n\u003cp\u003eAs a result, we may expect the longest Length of Spells (LOS) of Humidex exceeding 45\u003csup\u003e\u003cstrong\u003e\u0026deg;\u003c/strong\u003e\u003c/sup\u003eC as well as the average length such spells to increase rapidly after 2000\u0026ndash;2009. To quantify the same, we examine how the frequency and duration of the extreme Humidex (\u0026gt;\u0026thinsp;45\u0026deg;C) has changed over time (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Not only the frequency of short 3\u0026ndash;5 days spells have increased manyfold, the longest spells in a season has also increased significantly during this period (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). It is notable that, the longest duration of spells has changed from about 15 days during 1940\u0026rsquo;s to 1990\u0026rsquo;s and rapidly increased to more than 30 days (right scale in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb) in 2020\u0026rsquo;s. Also the average LOS has increased from about 5 days in early days to about 10 days in recent years while the average gap between spells has decreased from 40 days to 3 days in recent years (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). The other notable fact is that the interannual standard deviation of LOS was small about 2 days till about 1990\u0026rsquo;s and increased threefold to 6 days in recent years (red dashed line Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). This means that longer spells lasting 15\u0026ndash;20 days have become more common. The concomitant increase in both the location and scale parameters of the distribution of LOS implies a much rapid increase in extremes as compared to changes in only the mean or standard deviation. In contrast, interannual standard deviation of average gap used to be high (~\u0026thinsp;30 days) in early days reducing to ~\u0026thinsp;3 days in recent years (blue dashed line, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). This is essence of the adaptability challenge. Do the extreme moist heat spells also occur at the expense of milder more tolerable heat spells? If so, it would act to further increase the annual moist heat stress burden. To explore this aspect, we examine the frequency of Humidex under different categories. The range of Humidex depicted in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e is often divided into 7 categories (Table-1) with increasing level of discomfort and danger from exposure of the humid heat stress. The time series of frequency of occurrence of different categories of Humidex from 1940 to 2023 (Fig. S6) indicates that the frequency of occurrence of category-7 gradually increased from 1940\u0026rsquo;s while that of category 4 and 5 remain largely unchanged. The increase in frequency of categories-7 in recent decades has happened at the expense of decreasing frequency of 2\u0026ndash;3 and 6 categories, suggesting that the changing standard deviation has primarily reflects changes in the positive rather than the negative tail of the distribution of LOS.\u003c/p\u003e\n\u003cp\u003eThe warm tropical Indian Ocean together with Western Pacific warm pool (Fig. S7) are the sources of abundant moisture for the Indian summer monsoon weather disturbances. The tropical Indian Ocean is warming at a rate faster than any other world Oceans(\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e) and makes the atmosphere over the Indian monsoon region increasingly more unstable and responsible for the increasing trends of extreme daily rainfall events(\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e). The Humidex being a nonlinear function of the humidity, we envisage that the increasing trend of the frequency of extreme Humidex days is also strongly related to increasing trend of moisture content because of increasing trend of Indian Ocean SST. To gain some insight into this, we examine the spatial distribution of change in frequency of Humidex between recent decade (2014\u0026ndash;2023) and an earlier decade (1940\u0026ndash;1949) for three categories, namely category 5, 6 and 7 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). It is noted that lower Humidex values corresponding to category 5 or lower occur when the monsoon is in an \u0026lsquo;active\u0026rsquo; state with enhanced widespread rainfall and the ITCZ is north of 10\u003csup\u003e\u0026deg;\u003c/sup\u003eN while the higher Humidex values in categories 6 and 7 happen when the Indian monsoon is in a \u0026lsquo;break\u0026rsquo; state with widespread decreased rainfall north of 10\u0026deg;N and increased rainfall south of 10\u0026deg;N. This observation is again consistent with some recent studies(\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e) and supports our claim that the frequency of the extreme moist heat stress days are modulated by the MISO. The N-S dipole pattern of the change in the frequency is associated with this process. The pattern implies that for the category 5, short but intense weather disturbances within the \u0026lsquo;active\u0026rsquo; Inter-tropical Convergence Zone (ITCZ) over the Indian monsoon region in the recent decades lead to decrease in frequency of the lower Humidex values while for category 6 and category 7, more frequent and longer \u0026lsquo;break\u0026rsquo; spells on a \u0026lsquo;weaker\u0026rsquo; ITCZ over the Indian monsoon region during the recent decades lead to an increase in the frequency of the higher Humidex values. Consistent with the fact that the warm ocean provides the source of moisture, the decrease/increase of Humidex over land are contiguous with decrease/increase of the same over the Ocean.\u003c/p\u003e\n\u003ch3\u003eUniversality of the Adaptability Crisis\u003c/h3\u003e\n\u003cp\u003eTo get an insight on whether the adaptability crisis is unique to the Northeast India, we examine the annual exposure over the Indian monsoon region during the recent decade 2011\u0026ndash;2019 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea), its trends (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb), time series of Humidex exceeding 45\u003csup\u003e\u0026deg;\u003c/sup\u003eC at Dhaka and Jaipur (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ee) and the annual cycles of decadal-mean maximum Humidex at Dhaka and Jaipur (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec,d). Consistent with other studies (e.g. Saeed et al., 2021, Sojan and Srinivasan, 2024)(\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e), high exposure to extreme humid heat stress is experienced not only over the Northeastern region but also along the sub-Himalayan Gangetic plains, northwest India\u0026rsquo;s semi-arid region and parts of Pakistan. It is also notable that the eastern coast of India experiences exposure to extreme humid heat stress, while the western coast is spared from it. The time series of frequency of days in a year with Humidex exceeding 45\u003csup\u003e\u0026deg;\u003c/sup\u003eC at Dhaka and Jaipur (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ee) indicate that the nonlinear trend is like that over Guwahati (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea) with disruptive change in the trend taking place in the decade of 1980\u0026ndash;1989 instead of the decade of 1990\u0026ndash;1999 in case of Guwahati. Like Guwahati or Dhaka, the rainfall and moisture content along the western coast of India (west of the Western Ghat) are high and yet the exposure to Humidex exceeding 45\u003csup\u003e\u0026deg;\u003c/sup\u003eC along the western coast of India is negligible (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). The apparent dichotomy is due to the decadal mean Humidex along the western coast of India remaining below 45\u003csup\u003e\u0026deg;\u003c/sup\u003eC even in the latest 2010\u0026ndash;2019 decade (Fig. S8, S9). The monsoon winds produce a cooling off the western coast of India during the summer monsoon season, increase primary productivity(\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e) and keep the mean surface water temperature closer to 27\u003csup\u003e0\u003c/sup\u003eC (Fig. S6, also see Schott et al., 2009)(\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e) and leads to the mean air temperature during monsoon season colder than pre-monsoon season at around 27.5\u003csup\u003e0\u003c/sup\u003eC at two locations along the western coast (Mumbai and Panaji, Fig. S8,S9). The colder air temperature and associated lower moisture content of the coastal atmosphere led to mean Humidex remaining significantly below the critical level (~\u0026thinsp;40\u003csup\u003e0\u003c/sup\u003eC) even during the recent decade!\u003c/p\u003e\n\u003cp\u003eWhile the annual exposure could vary, the adaptability crisis arising from doubling the average LOS and reducing the gap between spells to about 3 days remains similar throughout the sub-continent. For example, while the humid season in both Guwahati and Dhaka is from May to September, the days Humidex exceeding 45\u003csup\u003e\u0026deg;\u003c/sup\u003eC is 100 for Guwahati and 140 for Dhaka. However, the average LOS has increased from 6 days to 10 days in both Jaipur and Dhaka while the average gap between spells decreased from 35 days to 3 days. In Jaipur, total annual exposure critical heat stress is about 1500 hours in 75 days making average daily exposure of 20 hours! That makes adaptation even more challenging in Jaipur.\u003c/p\u003e\n\u003cp\u003eTo illustrate that the adaptability crisis is not limited to Guwahati, Dhaka or Jaipur, we calculated the average LOS during the first decade (1940\u0026ndash;1949) and the last decade (2013\u0026ndash;2022) (Fig.s 7a,b) together with the average gap between spells during the first and the last decades (Fig.s 7c,d).\u003c/p\u003e\n\u003cp\u003eIt may be noted that over the entire region where the frequency of exposure to critical humid heat stress is increasing over Northeast Indian continent including Bangladesh, sub-Himalayan Gangetic plain and northwest Indian semiarid regions, the average LOS is increasing to 30 days while the average gap between spells is decreasing to 3-days in recent years (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea-d). In addition, like the examples shown over Guwahati, Dhaka and Jaipur, the interannual variability LOS has markedly increased and that of the gaps between spells decreased markedly over entire high-exposure regions of South Asia supporting that the adaptability challenge is universal (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ee-h)).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver the past couple of decades, the people living over Northeast Indian continent (including Bangladesh) experienced that the extreme humid heat spells have become longer and more frequent challenging the day-to-day activity during the summer monsoon season. The Northeast India being a key component of the Indian monsoon system and the world\u0026rsquo;s wettest region, it may be expected. However, neither national nor global studies focused or addressed the challenges in adaptation to exposure to more than 12 hours daily coming in continuous extreme humid heat spells lasting for more than 10 days sometimes lasting as long as 30 days. Up until 1990\u0026rsquo;s, adaptation to humid heat exposure was feasible due to small total number of days of exposure coming from short-duration spells with much longer gaps between spells for recovery. The situation changed dramatically from 2000\u0026rsquo;s when the people abruptly faced 3\u0026ndash;4 times larger number of days of exposure from much longer-duration spells with much shorter gaps for recovery. The adaptation crisis is not unique to the Northeast Indian sub-continent but generic to whole of South Asia. Unfortunately, recognition of the crisis has been lacking and in the absence of appropriate advisories, outdoor workers in the region are being dangerously exposed and no data available on how it is affecting the health of outdoor workers and contributing to their mortality. Our study quantifies the seriousness of the crisis and compelling evidence presented here provides basis for taking appropriate policy interventions for adaptation and mitigation.\u003c/p\u003e \u003cp\u003eWe unravel a dramatic increase in the number of days with a Humidex exceeding 45\u0026deg;C in Northeast India, that surged to more than 80 days in the 2020s, a fourfold increase from the roughly 20 days between the 1940s and 1990s. While the rate of increase in frequency of extreme heat-stress days between 1990 and 2020 should be faster than that of the surface temperature increase during the same period (Fig. S3) due to the combined contribution of temperature and humidity(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), but fails to explain the fourfold increase. It also fails to explain the disruptive increase in frequency in 2000\u0026rsquo;s. The dramatic increase in recent decades is a result of elevating the decadal mean of seasonal mean Humidex to above the critical level of 45\u0026deg;C in 2000\u0026rsquo;s (Fig. S4g). This results in most summer days without heavy rainfall and during extended periods of dry spells exceed the critical Humidex of 45\u0026deg;C. The cumulative exposure during the summer monsoon season (May-September) has risen to a maximum of about 800\u0026thinsp;\u0026plusmn;\u0026thinsp;265 hours in recent years, compared to approximately 200\u0026thinsp;\u0026plusmn;\u0026thinsp;92 hours before the 1990s. More Importantly, longest annual extreme moist heat spell days (LAMD) has increased threefold from 1990\u0026rsquo;s to more than 30 days in 2020\u0026rsquo;s from about 10 days in the early period. As a result, the average length of extreme moist heat spells (LOS) has more than doubled from about 5 days in early period to 10 days in recent period. This led to average gap between spells to reduce from more than 10 days to less than 3 days in recent years. The increasing length of the extreme Humidex in recent years are associated with longer duration of \u0026lsquo;break\u0026rsquo; spells of monsoon sub-seasonal oscillations and are consistent with expected increasing length of \u0026lsquo;break\u0026rsquo; spells(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) because of increasing frequency extreme El Nino events under global warming(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Our findings of association of the monsoon sub-seasonal oscillations with the increasing trend of extreme humid heat stress over the NEI are in tune with recent finding(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) that humid heat stress events are modulated by the monsoon sub-seasonal oscillations over the whole of Indian monsoon region.\u003c/p\u003e \u003cp\u003eOur findings have major implications on how the extreme humid heat spells are impacting health in the tropics. To quantify it better, the studies of association between extreme humid heat stress exposure with respect to longest length of extreme moist heat-stress spell (LLOS) as well as average annual extreme moist heat-stress spell (LOS) in addition to Total Annual Exposure (TAE) are needed. We also find the Weather Advisories in all tropical countries including India are using dry bulb temperature to indicate all extreme heat spells including moist heat spells. For example, a dry bulb temperature of 37\u0026deg;C even when mentioned that it is 5\u0026deg;C above normal does not communicate the seriousness of the situation that at 60% relative humidity, the feel like temperature is 53\u0026deg;C. Therefore, we also argue that weather advisories in tropical countries should prioritize use of heat stress indices such as Humidex rather than solely focusing on dry bulb temperature. Additionally, there is an urgent need for systematic research into the impact of heat stress on morbidity and the progression of heat stress-related illnesses. This research is crucial for developing effective adaptation and mitigation strategies tailored to the tropical context.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe primary source of data used in this study is the hourly ERA5 reanalysis data with a horizontal resolution of 0\u0026middot;25\u0026deg; x 0\u0026middot;25\u0026deg;(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). This data includes 2-meter air temperature and 2-meter dew point temperature, from which the Humidex is computed using a nonlinear combination. The reanalysis total temperature is also used to investigate the relationship with Humidex. Additionally, the COBE-SST(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) dataset is utilized to visualize the warming of the Indian tropical ocean.\u003c/p\u003e \u003cp\u003eThe study focuses on the Northeast part of the South Asian Summer Monsoon region, specifically the plain regions between 89\u0026deg;E to 98\u0026deg;E and 21\u0026deg;N to 30\u0026deg;N. The main study season is from May to September, coinciding with the peak of the summer monsoon​​. To examine the cumulative or mean frequency of heat spells, we excluded hilly and mountainous regions by masking spatial data using Digital Elevation Model (DEM) data with an altitude threshold of 200 meters or less (i.e., grid points within the green contour in the inset map of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec are included only). The DEM data provides an updated global topography and bathymetry grid at a spatial sampling interval of 15 arc seconds(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). For urban and rural grid points, data is selected based on comparative municipal development, specifically choosing Guwahati (25\u0026middot;5\u0026deg;N, 91\u0026middot;5\u0026deg;E; GHY) for the urban grid and Mankachar (25\u0026middot;1\u0026deg;N, 89\u0026deg;E; MKR) for the rural grid.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHumidex Calculation\u003c/h3\u003e\n\u003cp\u003eHumidex is an index indicating how hot the weather feels to an average person under humid conditions, combining temperature and humidity into one number reflecting perceived temperature. It can be calculated using the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{H}\\:=\\:\\text{T}\\:+\\:0\u0026middot;5555\\:\\times\\:\\:\\left(\\:6\u0026middot;11\\:\\times\\:\\:\\text{exp}\\left(\\:\\frac{5417\u0026middot;7530\\:\\times\\:\\:\\left(\\:\\frac{1}{273\u0026middot;16}\\:-\\:\\frac{1}{273\u0026middot;16\\:+\\:{\\text{T}}_{\\text{d}}}\\:\\right)}{\\text{T}}\\:\\right)\\:-\\:10\\:\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere,\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eT: The air temperature in degrees Celsius (\u0026deg;C).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eT\u003csub\u003ed\u003c/sub\u003e: The dew point temperature in degrees Celsius (\u0026deg;C). The dew point is the temperature at which air becomes saturated with moisture and water vapor starts to condense.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e0\u0026middot;5555: A constant used in the formula to scale the effect of the dew point temperature on the Humidex value.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e6\u0026middot;11: This is the vapor pressure of water in hectopascals (hPa) at the temperature of 0\u0026deg;C, a constant used in calculating the saturated vapor pressure.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eexp: The exponential function, which raises the mathematical constant e (approximately equal to 2\u0026middot;71828) to the power of the expression within the parentheses. It is used here to model the relationship between temperature and vapor pressure.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e5417\u0026middot;7530: This is a constant that comes from the Clausius\u0026ndash;Clapeyron relation, which describes the phase transition between two states of matter, in this case, water vapor and liquid water. It represents the energy required to change water from liquid to gas.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e273\u0026middot;16: These are constants representing the temperature in Kelvin for absolute zero plus 0.01 degrees Celsius and 0 degrees Celsius, respectively. This conversion is necessary because the exponential term in the equation involves temperatures in Kelvin.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e10: A constant subtracted to normalize the equation to typical conditions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe relationship between Humidex and the appropriate response for outdoor activity is detailed in the Occupational Health Clinics for Ontario Workers (OHCOW)(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) Humidex based heat response plan, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eHumidex-Based Heat Stress Guidelines and Recommended Preventive Measures for Workers.\u003c/b\u003e This table represents heat stress guidelines based on the Humidex index, outlining risk categories and recommended preventive measures for workers to ensure safety during hot weather conditions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003csup\u003e*\u003c/sup\u003e Humidex (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffective\u003csup\u003e**\u003c/sup\u003e Humidex (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuidelines\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 to 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;23\u0026middot;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupply water to workers on an \"as needed\" basis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 to 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.1 to 24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost Heat Stress Warning notice; encourage workers to drink extra water; start recording hourly temperature and relative humidity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 to 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1 to 25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost Heat Stress Warning notice; notify workers that they need to drink extra water; ensure workers are trained to recognize symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 to 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.1 to 26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWork with 15 minutes relief per hour; provide adequate cool water (10\u0026ndash;15\u0026deg;C); at least 1 cup (240mL) of water every 20 minutes; workers with symptoms should seek medical attention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 to 41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.1 to 27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWork with 30 minutes relief per hour; continue in addition to the provisions listed previously\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 to 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1 to 29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIf feasible, work with 45 minutes relief per hour; continue in addition to the provisions listed above\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnly medically supervised work can continue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Adjusted means adjusted for additional clothing and radiant heat\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e**Effective means adjusted for clothing\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAt air temperature of 30\u003csup\u003e\u0026deg;\u003c/sup\u003eC and relative humidity (RH) 70%, it feels like 42\u003csup\u003e\u0026deg;\u003c/sup\u003eC and at 80% RH it feels like 45\u003csup\u003e\u0026deg;\u003c/sup\u003eC while at air temperature of 35\u003csup\u003e\u0026deg;\u003c/sup\u003eC and RH 60% it feels like 50\u003csup\u003e0\u003c/sup\u003eC and at 70% RH it feels like 55\u003csup\u003e\u0026deg;\u003c/sup\u003eC (Fig. S10). Over the monsoonal regions of the tropics like the Northeast India, air temperature of 35\u003csup\u003e\u0026deg;\u003c/sup\u003eC to 38\u003csup\u003e\u0026deg;\u003c/sup\u003eC with 70% \u0026minus;\u0026thinsp;80% RH is common during May to September.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe maximum, minimum, and mean Humidex values are computed from hourly data from 1940\u0026ndash;2023. The study region is primarily Northeast India, but it also includes glimpses of heat spell frequency over the broader South Asian Summer Monsoon domain.\u003c/p\u003e \u003cp\u003eTo study the cumulative or mean frequency of heat spells over all grid points in Northeast India, the data from areas with altitudes below 200 meters is extracted. Different urban and rural differences are examined by selecting specific grid points representing Guwahati and Mankachar.\u003c/p\u003e \u003cp\u003eThe analysis includes visualizing the time series of the number of days the maximum Humidex exceeds 45\u0026deg;C at both urban and rural locations. Furthermore, the relationship between Humidex and precipitation is analyzed to understand the meteorological controls on extreme heat stress frequency.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo analyse trends in the frequency of critical humid heat stress days, we applied a continuous piecewise linear regression approach to the time series data, dividing it into two distinct segments. This method enabled us to quantify shifts in the rate of increase across different periods, revealing changes in heat stress trends over time. A continuous piecewise linear function includes breakpoints that signify the transition points between line segments, allowing us to model a slower trend in the earlier period and a more accelerated trend in recent decades. Since the exact location of the breakpoint was unknown, we employed a global optimization technique, differential evolution, to identify the optimal year. This technique was selected for its ability to explore a wide range of potential breakpoints and minimize fitting error. Specifically, it iteratively tested different breakpoint positions, applying least squares fitting to identify the location that best represented the natural transition in trends with minimal error(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpatial changes in the frequency of Humidex values are examined by comparing the means from present decades (2014\u0026ndash;2023) to past decades (1940\u0026ndash;1949) over the South Asian Summer Monsoon (SASM) region. This helps to understand the broader spatial patterns and regional variations in humid heat stress.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP.S. and R.M. extend their appreciation to Cotton University for providing the essential infrastructure and resources necessary for conducting this research. BNG thanks Science and Engineering Research Board (SERB), Government of India for supporting the computational facility for this work and Gauhati University for the Honorary Professor of Excellence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Conceptualization: PS, BNG\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Methodology: PS, BNG, JMM\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Investigation: PS\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Data Analysis (Major): PS\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Data Analysis (Partial): DB, PB\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Visualization: PS\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Supervision: BNG\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Writing\u0026mdash;original draft: BNG\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Writing\u0026mdash;review \u0026amp; editing: JMM, RM, PS, BNG\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in this study are publicly accessible from reputable sources. The ERA5 data were obtained from the Complete ERA5 Global Atmospheric Reanalysis provided by the Copernicus Climate Data Store. Global bathymetry data from SRTM15+ were sourced from the Scripps Institution of Oceanography. Additionally, COBE monthly SST data were retrieved from NOAA\u0026apos;s Physical Sciences Laboratory.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All computations and analyses for this study were conducted using open-source software tools, ensuring transparency and reproducibility. These include the Climate Data Operators (CDO), available at (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-complete?tab=overview, and the NCAR Command Language (NCL), developed by the Computational \u0026amp; Information Systems Laboratory at the National Center for Atmospheric Research (NCAR) and sponsored by the National Science Foundation (https://www.ncl.ucar.edu/). Additionally, Python, an open source programming language widely used for scientific data processing and visualization, was employed, and its resources can be accessed at https://www.python.org/downloads/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWaidelich P, Batibeniz F, Rising J, Kikstra JS, Seneviratne SI (2024) Climate damage projections beyond annual temperature. Nat Clim Chang 14:592\u0026ndash;599\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKotz M, Levermann A, Wenz L (2024) The economic commitment of climate change. 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(2024)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Cotton University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Humidex, Dry bulb temperature, Wet bulb temperature, Hours of Exposure","lastPublishedDoi":"10.21203/rs.3.rs-6273180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6273180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile the explosive increase in extreme humid heat stress exposure from 2000’s over South Asian monsoon region is challenging human adaptability leading to productivity and mortality loss, factors responsible remain poorly constrained. Here, we unravel that the disruptive regional climate change of decadal-mean maximum Humidex exceeding 45°C to be the primary cause. Over Northeast India, it results in the exposure of extreme heat-stress during the monsoon season rising fourfold to 80 days or 800 ± 278 hours and makes the longest annual extreme moist heat-spell duration increase threefold to 30 days in the 2020’s. The adaptation crisis arises from the average length of spells doubling to 10 days while the average gap between spells decreasing to 3 days. Our findings of changes in characteristics of moist heat spells holds for a large fraction of South Asia, and highlight the urgent need for data on impact of long-spells of continued exposure on human physiology for appropriate advisories and policy interventions.\u003c/p\u003e","manuscriptTitle":"Heightened Adaptability Challenges from Extreme Humid Heat Stress for South Asia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 05:03:32","doi":"10.21203/rs.3.rs-6273180/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"068f322a-3b27-46d9-bc44-de186bdec409","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46038412,"name":"Atmospheric Sciences"}],"tags":[],"updatedAt":"2025-03-25T05:03:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-25 05:03:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6273180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6273180","identity":"rs-6273180","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}