Precipitation pattern has changed over many regions in recent decades. There are evidences of increased heavy precipitation and decreased light precipitation in widespread parts of the globe due to global warming. Many studies over Indian region focus on heavy precipitation and risk of floods. But few works discuss the changes in light precipitation and risk of droughts. In this study, changes in total dry days, prolonged dry spells, light precipitation, and risk of drought as indicated by Modified Palmer Index (MPI) over India during six decades (1951–2010) are examined quantitatively in the context of global warming. It is found that there are increases of 49% ± 21% and 33% ± 17% in prolonged dry spells and total dry days, respectively, over India for each degree Kelvin (K) increase in global mean temperature. There is an increase of 51% ± 24% K −1 in drought index MPI (<= − 2.0). There is also a reduction of 31 ± 14% K −1 in light precipitation days over India. These changes are more severe over northeastern and western part of India. Increases in prolonged dry spells, total dry days, and decreases in light precipitation relate well with the increases in drought index MPI (<= − 2.0). These results suggest that there is an increased risk of drought due to increased prolonged dry spells, total dry days, and decreased light precipitation days over India as a result of global warming.

1 Introduction Significant changes in precipitation pattern are expected in a warming environment [Trenberth et al., 2003]. Recent studies have shown that worldwide there are extensive increases in heavy precipitation and decreases in light and moderate rain [Liu et al., 2005; Groisman et al., 2005; Fujibe et al., 2005; Goswami et al., 2006; Lau and Wu, 2007, 2011; Lenderink and Meijgaard, 2008; Liu et al., 2009; Shiu et al., 2012; O’Gorman, 2012; Villarini et al., 2013]. These changes in the precipitation patterns are attributed to global warming [Trenberth et al., 2003]. Water‐holding capacity of boundary layer over ocean increases by about 7% K−1 due to global warming according to Clausius‐Clapeyron equation. Trenberth et al. [2003] suggested that the increase in precipitation intensity of storms should follow the same rate as environmental moisture. This rate of increment could even be more because extra latent heat released from the increased water vapor could intensify the storm. This intensified system depletes the atmospheric moisture available for light and moderate rain events. So in a warming environment both increase in heavy precipitation events as well as decrease in light rain events can be expected. This change of pattern was confirmed by many researchers using modeling results and observations [Sun et al., 2007; Liu et al., 2009; Shiu et al., 2012]. In addition, regions near tropics are more susceptible to such changes in precipitation pattern in a warming environment [Liu et al., 2009]. Soman and Krishna Kumar [1990] investigated some aspects of daily rainfall distribution over India during southwest monsoon period. In recent decades many parts of Indian region experienced frequent serious floods and droughts [De et al., 2005]. Economy of India is highly dependent on the agriculture which in turn is greatly affected by change in precipitation patterns. Drought is a natural hazard that can have devastating effect on regional agriculture, water resources, and the environment. Prediction of floods and droughts is very difficult; nevertheless, knowledge of trends and characteristics of these events would be valuable for disaster preparedness. Increased temperature and decreased precipitation may contribute to significant changes in drought. Global climate models indicate that in a warmer climate droughts may become longer lasting and more severe in drought‐prone regions because of enhanced evaporation [Gregory et al., 1997]. While many previous investigations have analyzed heavy precipitation due to global warming over India [Rupa Kumar et al., 1992; Sinha Ray and Shiwale, 2001; Goswami et al., 2006; Rajeevan et al., 2008; Dash et al., 2009], few efforts have focused on the changes in total dry days, prolonged dry spells, light precipitation days, and risk of droughts. Dash et al. [2009, 2011] investigated the pattern of rainfall over India and found that frequencies of light precipitation days have significantly decreased in the last half century. They also pointed out that prolonged dry spells showed an increasing trend. However, their studies did not quantify these changes. Krishnan et al. [2000] presented results of diagnostic analysis of observations and complementary experiments with a simple numerical model that enabled them to synthesize the morphology and dynamics of “breaks” in the Indian summer monsoon. They observed that an abrupt movement of anomalous Rossby waves from the Bay of Bengal into northwest and central India marks the beginning of a dry spell. They found that low‐latitude Rossby wave dynamics in the presence of a monsoon basic flow, which is driven by a steady north‐south differential heating, is a primary physical mechanism that controls the dry spells. In this study daily data at 1° × 1° resolution in India from India Meteorological Department (IMD) are analyzed to investigate changes in the characteristics of precipitation as a function of global mean temperature by using a new approach which focuses on interannual differences rather than time series. Changes in light precipitation days, total dry days, and prolonged dry spells are examined during past 60 years (1951–2010). A data set of Modified Palmer Index (MPI) [Bhalme and Mooley, 1979] based on precipitation and temperature is also calculated and analyzed to study the risk of drought in India. In addition, we try to address the observed frequent droughts over Maharashtra, Rajasthan, Gujrat, Arunachal Pradesh, Assam, Manipur, Nagaland, Mizoram, and Kolkata during past few decades [De et al., 2005] by investigating northeastern and western part of Indian region separately. It is seen that even heavy rainfall regions over northeastern part of India have experienced drought‐like situations during the recent years (e.g., 1990, 1994, 1996, 1998, 2005, 2006, and 2009).

2 Data Used and Area of Study A high‐resolution precipitation data set (1° × 1°) developed by IMD over India [Rajeevan et al., 2006] is used in the present study. This data set is developed using precipitation observations from more than 1800 rain gauges over India with quality control. These rain gauge observations are interpolated using Shepard [1968] inverse distance weighted interpolation technique into regular grid of 1° latitude × 1° longitude. The daily precipitation data set for the years 1951–2010 is used for the analysis of precipitation events over India. Global mean temperature from National Climate Data Center (NCDC) is also used in this study. Drought assessment is usually done with drought indices. Many drought indices such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration (SPEI), and Palmer Drought Severity Index (PDSI) are used to assess the severity and spatial extend of the drought. Each of these indices has their own advantages and limitations [Vicente‐Serrano et al., 2012]. Among various drought indices, the Palmer Drought Severity Index (PDSI, Palmer 1965) is one of the most widely used. PDSI is a climatic water balance index that considers precipitation and evapotranspiration anomalies and soil water‐holding capacity. It is a good measure of cumulative effect of atmospheric moisture supply and demand. Positive PDSI values show wetness, while negative values show dryness. PDSI uses precipitation as well as surface air temperature as inputs, in contrast to many other drought indices that are based on precipitation only [Keyantash and Dracup, 2002]. However, it was found by Bhalme and Mooley [1979] that PDSI fails to describe realistically the drought conditions over India. They modified the Palmer index to suit the tropical weather conditions in India. We have estimated the Modified Palmer Index (MPI) using IMD precipitation data at 1° × 1° grid box following Bhalme and Mooley [1979]. We have used MPI data for the period 1951–2010 in this study. Drought is a phenomenon which may occur simultaneously across multiple temporal scales. A short period of particular dryness may be embedded within a long‐term drought. The response of various hydrological systems to precipitation can vary as a function of time. The study areas are described in Figure 1. Oceanic regions are not a part of this study. Figure 1 Open in figure viewer PowerPoint Areas of study. Black box (represented by “I”) shows entire Indian region (except southern and northern tips) used in this study, “red” denotes northeast part (represented by “NEI”), “blue” denotes western part (represented by “WI”), and “green” denotes central part (CI).

3 Methodology We adopt the methodology developed by Liu et al. [2009]. Liu et al. [2009] found that the change in precipitation characteristics is better correlated with the global temperature than local temperature as water vapor in most regions is regulated by large areas in nearby oceans of which the temperature tends to correlate well with the global temperature. For this reason, global temperature is used in this study. However, we also analyzed the changes in dry spells/light rainy days as a function of local temperature over India, and result is presented in the supporting information (Figure S2). The capability of this technique can be demonstrated by a study of the changes in heavy precipitation over central India and comparing the results with those obtained by Goswami et al. [2006] and Rajeevan et al. [2008]. IMD rainfall data for the monsoon period of 1951–2010 are sorted into 10 bins of equal precipitation amount in increasing precipitation intensity. These bins are calculated as 6.52, 11.86, 17.64, 23.84, 29.82, 38.61, 49.87, 67.60, 99.36, and >99.36 mm/d for central India during monsoon season. Difference in top 10% heavy precipitation (precipitation > 99.36 mm/d) ΔP of any 2 years within 1951–2010 is divided by the global temperature difference ΔT for those 2 years. ΔP/ΔT which is the slope between ΔP and ΔT (or change of P for unit change of T) is plotted against ΔT in Figure 2 to explore the relationship between change in top 10% heavy precipitation as a function of global temperature. Figure 2 Open in figure viewer PowerPoint ΔP/ΔT of top 10% and top 1% bin of precipitation over central India during monsoon season as a function of ΔT. Vertical bar denotes the 1 standard deviation. In Figure 2, ΔP between any 2 years in 1951–2010, with a total 60 × 59/2 = 1770 independent data points in 60 years, is included. The number of points is 29.5 times the data points (60) if time series are used to deriveΔP/ΔT. The vertical bars denote 1 standard deviation range for the group of 20 data points. As ΔT increases, there is a reduction of scattering of points and mean value of ΔP/ΔT converges toward a constant (~55% ± 24% K−1). The horizontal line passing through the mean value of ΔP/ΔT of the group at the largest ΔT near 0.57 is well within the 1 standard deviation ranges over the entire range of ΔT. This suggests that, within the 1 standard deviation range, ΔP is statistically linearly proportional to ΔT throughout the entire range of ΔT. In practice, the mean value of ΔP/ΔT of the group at the largest ΔT, with a value of 55% ± 24% K−1 can be regarded as the representative of values of the ΔP/ΔT ratio of the top 10% heavy precipitation for a wide range of ΔT around 0.1 to 0.57 K. There is an increase of 0.68 K in global temperature (from NCDC temperature anomaly data) during 1951–2010, so the total increase in heavy precipitation during last three decades is about 37.4 ± 16.32%, which scales to an increase of about 6.23 ± 2.72% per decade in the heavy precipitation. This is in good agreement with the results of about 6% and 10% increase per decade in top 10% heavy precipitation derived by Rajeevan et al. [2008] and Goswami et al. [2006], respectively. We also derive an increase of about 109% ± 51% K−1 in very heavy precipitation (top 1% bin, rainfall > 150 mm/d), about double of the values observed for top 10% precipitation. So result obtained using interannual difference technique of Liu et al. [2009] is consistent with that obtained by Rajeevan et al. [2008] and Goswami et al. [2006].

4 Results and Discussions We apply this interannual difference technique to study the risk of drought over Indian region using yearly data in the period 1951–2010. Following the IMD, we define prolonged dry spells as events when there is no rain for five or more consecutive days. An increase in prolonged dry spells and total dry days may trigger a drought situation. Figure 3 shows the relationship between ΔD/ΔT and ΔT for the prolonged dry spells and total annual dry days over India, western, and northeastern India during years 1951–2010. It can be interpreted from Figure 3a that during last 60 years, prolonged dry spells and total dry days over India have increased by about 49% ± 21% K−1 and 33% ± 17% K−1, respectively. Figure 3 Open in figure viewer PowerPoint ΔD/ΔT of prolonged dry spells and dry days as a function of ΔT: (a) India, (b) western India, and (c) northeastern India. Vertical bar denotes the 1 standard deviation. There are 53% ± 28% K−1 and 69% ± 38% K−1 increases in prolonged dry spells over western (Figure 3b) and northeastern India (Figure 3c), respectively. Changes in total dry days are more severe over western and northeastern part of India as compared to entire India. So the impact of climate change is significantly greater over these regions as compared to entire India. Total increases in prolonged dry spells during last six decades are about 46.92 ± 25.84% and 36.04 ± 19.06% over northeastern and western Indian region, respectively. These scale to increases in prolonged dry spells of about 7.82 ± 4.30% and 6.0 ± 3.17% per decade over northeastern and western India, respectively. Changes in dry spells and nonrainy days are less for the entire Indian region as compared to northeastern and western part. This may be attributed to the fact that rate of changes in dry spells/light rainy days during monsoon period is more as compared to the rate of change during entire season (Figure S3 in the supporting information) and northeastern and western part of India receives most of its rainfall during summer monsoon, while southern and northern part (which are the parts of entire India) receives most of the rainfall during premonsoon/postmonsoon period. Differences in rate of changes in dry spells, nonrainy days, and light precipitation over northeastern, western, and entire Indian may also be attributed to different topography over these regions due to dependence of monsoon rainfall over topography [Sinha et al., 2013]. IMD annual rainfall data for the period of 1951–2010 over India are sorted into 10 bins of equal precipitation amount in increasing precipitation intensity. These bins are calculated as 3.54, 8.62, 14.24, 20.17, 25.41, 31.24, 38.42, 56.25, 78.94, and >78.94 mm/d for India during 1951–2010. Using interannual difference technique we found that there is reduction of about 22% ± 12% K−1 in bottom 30% light precipitation. The decrease in light precipitation coupled with the increases in heavy precipitation implies an enhancement in the precipitation intensity in India. Figure 4 shows the changes in days of bottom 30% light precipitation over India, northeastern, and western India. It can be seen that there are reductions of about 31% ± 14% K−1, 42% ± 21% K−1, and 50% ± 27% K−1 in days of bottom 30% light precipitation over India (Figure 4a), western (Figure 4b), and northeastern India (Figure 4c), respectively. Figure 4 Open in figure viewer PowerPoint ΔD/ΔT of days of bottom 30% light precipitation as a function of ΔT: (a) India, (b) western India, and (c) northeastern India. Vertical bar denotes the 1 standard deviation. Light precipitation permits water more time to soak into the soil, resulting in little surface runoff. Thus, light precipitation is very crucial to sustaining soil moisture during rainy season [Wu et al., 2012; Sun et al., 2006; Wang, 2006]. Reductions in frequency of light precipitation can increase the risk of drought events as seen over different parts of the globe [Yan and Yang, 2000; Quian et al., 2010; Lei and Duan, 2011]. Increase in prolonged dry spells, decrease in light precipitation, and rainy days may suggest weakening of the Indian summer monsoon circulation over India. This hypothesis of a weakening of monsoon circulation is supported by the reductions in low‐level wind fields [Dash et al., 2009] and easterly jet [Sreekala et al., 2013; Bansod et al., 2012] in warming environment. We also attempted to separate the impact of natural forcing from that of global warming by considering only non‐El Niño and non‐IOD years (1951, 1952, 1953, 1956, 1959, 1960, 1962, 1966, 1967, 1969, 1971, 1976, 1977, 1979, 1990, 1993, 1995, 2001, 2002, 2003, 2005, 2006, 2008, 2009, and 2010) and analyzed the changes in dry spells/light rainy days. Results are presented in the supporting information (Figure S1). It may be noted that impact of global warming is less as compared to combined impact of warming and natural forcing on changes in dry spells/light rainy days. However, this difference is within the standard deviation of both the data set. The change in risks of drought and severe drought indicated by MPI ≤ −2.0 and ≤ −3.0, respectively, is evaluated in Figure 5. It may be seen that risks of drought (MPI ≤ −2.0) are increased by about 51% ± 24% K−1, 71% ± 31% K−1, and 66% ± 27% K−1 over India (Figure 5a), western (Figure 5b), and northeastern India (Figure 5c), respectively. These scale to increases of about 5.78 ± 2.72%, 8.04 ± 3.51% and 7.48 ± 3.06% in risk of drought per decade over the three regions, respectively. Risks of severe drought (MPI ≤ −3.0) are increased by about 74% ± 33% K−1, 83% ± 39% K−1, and 79% ± 37% K−1 over India, western, and northeastern India, respectively. Increases in risk of severe drought are more as compared to those in drought. In addition, northeastern and western parts of Indian region are more susceptible to these changes. These results are consistent with more frequent droughts observed during the past few decades in Maharashtra, Rajasthan, Gujrat, Arunachal Pradesh, Assam, Manipur, Nagaland, Mizoram, and Kolkata which are located in the northeastern and western part of India [De et al., 2005]. Figure 5 Open in figure viewer PowerPoint Changes in risk of drought and severe drought indicated by MPI (≤ − 2.0, ≤ − 3.0) as a function of ΔT: (a) India, (b) western India, and (c) northeastern India. Vertical bar denotes the 1 standard deviation. The consistency among Figures 3-5 has two important implications. One is the number of dry days, prolonged dry spells, the decrease of light rain, and the low value of MPI are consistent indicators of high risk of droughts. The other is that these parameters may be closely related. In fact, in the case of dry days and light rain, a casual relationship may exist. This can be illustrated in Figure 6. Figure 6 Open in figure viewer PowerPoint (a) Time series of anomaly (%) of prolonged dry spells, total dry days, MPI, and bottom 30% precipitation. Scatterplots between anomalies (%) of bottom 30% precipitation and (b) prolonged dry spells, (c) total dry days, and (d) MPI (<= − 2.0). Figure 6a shows time series of anomaly of prolonged dry spells, total dry days, MPI (<= − 2.0) and bottom 30% precipitation. It may be seen from the figure that increase in risk of drought indicated by MPI (<= − 2.0) is consistent with increase in total dry days and prolonged dry spells and decreases in light precipitation (bottom 30% rain). Five extraordinary drought years (1972, 1987, 2002, 2004, and 2009) can be clearly spotted in the figure with sharp peaks in drought index anomaly (MPI ≤ −2.0). According to IMD report (Monsoon Monograph, http://www.imd.gov.in/section/nhac/dynamic/MM2.pdf), the seasonal rainfall over India was 76.1 % of its long‐period average in 1972, whereas it was 80.6%, 80.8, 86.2%, and 78.2, respectively, in 1987, 2002, 2004, and 2009. These sharp peaks in MPI anomaly are well observed with positive sharp peaks in anomaly of total dry days and prolonged dry spells as well as negative sharp peaks in anomaly of bottom 30% light precipitation. Bhalme and Mooley [1980] analyzed the drought year 1972 using Modified Palmer Index. They found that weaker meridional pressure gradient, larger northward seasonal shift of monsoon trough, large number of break spells (total 14), and smaller frequencies of depressions were the possible reasons behind 1972 drought year. Krishnamurti et al. [1989] attributed the drought year 1987 to the El Niño‐related warm SST anomaly, warm SST over near equatorial southern Indian Ocean, formation of a negative stream function anomaly at 200 mb over the Tibetan Plateau and in the upper troposphere north of the Indian subcontinent. Drought years 2002 and 2009 were attributed to the unfavorable Equatorial Indian Ocean Oscillation and El Niño [Gadgil et al., 2003; Francis and Gadgil, 2010]. Two noticeable valleys during the years 1961 and 1988 in MPI anomaly curve represent good monsoon years with the seasonal rainfall of 121.8% and 119.3% according to IMD report. All standout peaks and valleys in MPI are well reflected by the corresponding changes in total dry days, prolonged dry spells, and bottom 30% light precipitation, suggesting a causal relationship among them. There are good high anticorrelation coefficients of about −0.88 and −0.90 between prolonged dry spells and the bottom 30% precipitation (Figure 6b) and MPI and bottom 30% precipitation (Figure 6d), respectively. More remarkably, there is a high anticorrelation coefficients of −0.87 between the total dry days and bottom 30% light precipitation (Figure 6c). It is well known that a good correlation does not necessarily imply a casual relationship. However, the extreme tight anticorrelation between the total dry days and bottom 30% light precipitation is unlikely a coincidence. Moreover, in theory the increase in precipitation intensity with shifting of light to heavy precipitation should result in an increase in total dry days. Therefore, we believe that the tight anticorrelation does support a casual relationship between changes in total dry days and bottom 30% light precipitation. Similar argument can be made for changes in prolonged dry spells and MPI. In summary we propose that decreases in light precipitation coupled with increases in total dry days and prolonged dry spells as a result of increase in global temperature can increase the risk of droughts in India.

5 Summary and Conclusion Data over Indian region is analyzed to explore the changes in precipitation pattern with focus on risk of drought during 1951–2012. Interannual difference technique is used for this purpose. The changes in prolonged dry spells, light precipitation days, and drought indices are quantitatively linked to global temperature change. However, these changes can also be influenced by natural forcing like Indian Ocean Dipole (IOD), El Niño–Southern Oscillation (ENSO) and internal variability of monsoon. The results presented in this paper point to increasing risk in drought over India, in particular over the northeastern and western part. Prolonged dry spells and total dry days are increased by 5.6 ± 2.38% and 3.7 ± 1.92% per decade over India, respectively. Over northeastern and western part of India prolonged dry spells increase by 7.8 ± 4.30% and 6.2 ± 3.17% per decade, respectively. There is a reduction of about 3.5 ± 1.6% per decade in days of light precipitation over India. This reduction is more severe over northeastern and western part of India. There is a significant increase of 8.38 ± 3.74% per decade in the risk of severe drought indicated by MPI (≤ −3.0) over India. There is about 0.74 K rise in temperature globally during 1906 and 2005 [Intergovernmental Panel on Climate Change, 2007]. So India already has experienced increases of about 36.26 ± 14.31% (49 × 0.74) and 24.42 ± 13.19% (33 × 0.74) in prolonged dry spells and total dry days, respectively. Over northeastern and western Indian these increases in prolonged dry spells are 51.06 ± 24.32% and 39.22 ± 15.43%, respectively. Days of light precipitation have reduced by about 22.94 ± 10.74%, 31.08 ± 15.71%, and 37.37.14 ± 20.21% over India, western, and northeastern part, respectively. Moreover, there is about 54.76 ± 24.42% increase in the risk of severe drought indicated by MPI (≤ −3.0). Results presented in this study suggest that increases in total dry days, prolonged dry spells, and reductions in light precipitation might have played an important role for a significant increase in the risk of drought over India as a result of global warming. The socioeconomic impact of the increasing risk of droughts is very large in India [De et al., 2005]. Given the global inaction in the mitigation of global warming, adaptation actions such as water resource managements are imperative.

Acknowledgments The gridded rainfall data have been obtained from India Meteorological Department (IMD). We gratefully acknowledge the funding for this work from National Science Council of Taiwan under grant NSC96‐2111‐M‐001‐005‐MY3.

Supporting Information Filename Description AuxiliaryMaterial.docWord document, 760 KB Text S1 fs01.pdfPDF document, 80.2 KB Figure S1 fs02.pdfPDF document, 115.3 KB Figure S2 fs03.pdfPDF document, 185 KB Figure S3 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.