Drought is one of the most frequent natural disasters occurring in Pakistan and has a great influence on livelihood, agriculture, and economy. The availability of long‐term high‐quality reanalysis products over Pakistan has been of great concern in recent decades. Here, we conduct drought assessment in Pakistan based on the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) at 3, 6, and 12 months timescales during 1983–2018. We use long‐term in situ observations to evaluate the accuracy of reanalysis products, including Climatic Research Unit (CRU TS), National Centers for Environmental Prediction version II (NCEP‐2), European Centre for Medium‐Range Weather Forecasts Version‐5 (ERA‐5), and Modern‐Era Retrospective analysis for Research and Applications version II (MERRA‐2). The main results are summarized as follows: (a) drought indices and drought areas assessed from reanalysis products are relatively more representative of historical droughts that had occurred in southern Pakistan and overestimation is evident for drought severity in western than eastern Pakistan; (b) statistically significant increasing trends (1984–1998 and 2000–2010) in monthly drought areas and occurrence are evident by CRU TS and MERRA‐2 in dominant arid and semiarid regions; (c) climate variables and drought features of southern Pakistan are best represented by CRU TS and MERRA‐2, while that of southwestern and western parts are best represented by ERA‐5; (d) the Nash–Sutcliffe efficiency (NSE) results range from −2 to 1, where the NSE of SPEI values (−1.0) show relatively weaker than SPI values (0.5) in most parts of the regions, specifically in the southern Pakistan; (e) a strong positive linear relationship on a monthly scale is evident in CRU TS, MERRA‐2, and ERA‐5 exhibiting relatively high correlation coefficient (0.84), except for NCEP‐2. Furthermore, the SPEI results are found to be better than SPI; thus, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change.

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Myanmar is located in a tropical region where temperature rises very fast and hence is highly vulnerable to climate change. The high variability of the air temperature poses potential risks to the local community. Thus, the current study uses 42 synoptic meteorological stations to assess the spatiotemporal changes in air temperature over Myanmar during 1971–2013. The nonparametric sequential Mann-Kendall (SqMK), linear regression, empirical orthogonal function (EOF), Principal Component Analysis (PCA), and composite analysis were used to assess the long-term trends in maximum (Tmax) and minimum (Tmin) temperature series and their possible mechanism over the study region. The results indicate that the trend of Tmax has significantly increased at the rates of 90% in summer season, while the Tmin revealed a substantial positive trend in winter season time series with the magnitude of 30%, respectively. Moreover, during a rapid change of climate (1995‒2013) we observed an air temperature increase of 0.7 °C. The spatial distributions of EOF revealed relatively warmer temperatures over the whole region except the south in the summer; however, a similar pattern can be seen for the rainy season and winter, implying warming in the central part and cooling in the northern and southern parts. Furthermore, the Indian Ocean Dipole (IOD) influence on air temperature over Myanmar is more prevalent than that of the El Niño Southern Oscillation (ENSO). The result implies that the positive phase of the IOD and negative phase of the Southern Oscillation Index (SOI; El Niño) events led to the higher temperature, resulting in intense climatic extremes (i.e., droughts and heatwaves) over the target region. Therefore, this study’s findings can help policymakers and decision-makers improve economic growth, agricultural production, ecology, water resource management, and preserving the natural habitat in the target region.

In this study, we investigated the interdecadal variability in monsoon rainfall in the Myanmar region. The gauge-based gridded rainfall dataset of the Global Precipitation Climatology Centre (GPCC) and Climatic Research Unit version TS4.0 (CRU TS4.0) were used (1950–2019) to investigate the interdecadal variability in summer monsoon rainfall using empirical orthogonal function (EOF), singular value decomposition (SVD), and correlation approaches. The results reveal relatively negative rainfall anomalies during the 1980s, 1990s, and 2000s, whereas strong positive rainfall anomalies were identified for the 1970s and 2010s. The dominant spatial variability mode showed a dipole pattern with a total variance of 47%. The power spectra of the principal component (PC) from EOF revealed a significant peak during decadal timescales (20–30 years). The Myanmar summer monsoon rainfall positively correlated with Atlantic multidecadal oscillation (AMO) and negatively correlated with Pacific decadal oscillation (PDO). The results reveal that extreme monsoon rainfall (flood) events occurred during the negative phase of the PDO and below-average rainfall (drought) occurred during the positive phase of the PDO. The cold phase (warm phase) of AMO was generally associated with negative (positive) decadal monsoon rainfall. The first SVD mode indicated the Myanmar rainfall pattern associated with the cold and warm phase of the PDO and AMO, suggesting that enhanced rainfall for about 53% of the square covariance fraction was related to heavy rain over the study region except for the central and eastern parts. The second SVD mode demonstrated warm sea surface temperature (SST) in the eastern equatorial Pacific (El Niño pattern) and cold SST in the North Atlantic Ocean, implying a rainfall deficit of about 33% of the square covariance fraction, which could be associated with dry El Niño conditions (drought). The third SVD revealed that cold SSTs in the central and eastern equatorial Pacific (La Niña pattern) caused enhance rainfall with a 6.7% square covariance fraction related to flood conditions. Thus, the extra-subtropical phenomena may affect the average summer monsoon trends over Myanmar by enhancing the cross-equatorial moisture trajectories into the North Atlantic Ocean.

The present study assessed the spatiotemporal variation of summer monsoon precipitation and its potential drivers in Myanmar, utilizing monthly precipitation data from forty‐six (46) synoptic meteorological stations spanning 1981–2020. The nonparametric statistical Mann–Kendall (MK), Sequential Mann–Kendall (SQMK) test, Empirical Orthogonal Function (EOF), and Probability Distribution Function (PDF) were used to determine the spatiotemporal monsoon precipitation trends and variability over the study period. The results show that higher precipitation occurs during June, July and August (peak monsoon period), while low precipitation was detected in May (onset month), September and October (withdrawal monsoon period), respectively. Moreover, abrupt change in precipitation is observed after 1990 with a significant (95% confidence level) increasing trend from 2000 to 2020. Decadal precipitation experienced the highest fluctuation during 2011–2020, a positive shift and increased frequency in recent decades. The spatial trends for monthly and seasonal precipitation vary from station to station and region to region due to a fluctuated shift of climatic dynamics. During dry conditions, less cloud liquid water suppressed relative humidity and high air temperature were exhibited, thus implying less precipitation in the region. However, the wet years revealed strong moisture/water vapour into the inland regions from the ocean, increased relative humidity, and suppressed air temperature. In addition, no significant relationship was found between El‐Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and monsoon onset timing with precipitation variability over Myanmar. This study provides essential information on manageable climate adaptation, mitigation and weather forecasting strategies in Myanmar.