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Pakistan is located in one of the fast temperature rising zones and hence, highly vulnerable to climate change. The dynamic variations of the maximum (Tmax) and minimum (Tmin) temperatures pose potential risks to the local people. Thus, the present study assessed spatiotemporal changes in Tmax and Tmin over China-Pakistan Economic Corridor (CPEC) during 1980–2016 based on the 48 meteorological stations across the CPEC. The non-parametric Mann-Kendall (MK), Sen's Slope (SS) estimator, Sequential Mann-Kendall (SQMK), and least square method tests were used to assess the long-term trends in Tmax and Tmin time series during 1980–2016. The results indicate that the trend of Tmax has significantly increased at the rates of 0.22, 0.37, 0.20, 0.23, and 0.31 °C per decade in winter, spring, summer, autumn, and annual time scales, respectively. Similarly, the Tmin exhibited a significant positive trend in winter, spring, summer, autumn, and annual time series with the rates of 0.33, 0.39, 0.25, 0.27, and 0.36 °C per decade, respectively. The spatial distributions of Tmax and Tmin represent a warming trend over the whole country; however, the seasonal and annual Tmax (Tmin) exhibited sharp increasing trends in the northern and southwestern mountainous (southern, southwestern and southeastern) regions of the country. According to the mutation test, most of the abrupt changes in seasonal and annual Tmax and Tmin trends have been detected during 1995–2010. The present study recommends that forthcoming studies should focus on the factors responsible for the spatial and temporal variability of Tmax and Tmin in the target region.

This study was designed to evaluate the spatiotemporal performance of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models in the historical simulation and future projections of minimum (Tmin), maximum (Tmax), and mean temperature (Tmean) over South Asia (SA) during global warming of 1.5 oC, 2 oC, and 3 oC targets under RCP4.5 and RCP8.5 scenarios. It is worth mentioning that the present study is the first of its kind to use such a large number of CMIP5 models to project future changes in Tmin, Tmax, and Tmean over SA using three different warming thresholds. The results show that CSIRO–MK3–6–0, MIROC–ESM–CHEM, CNRM–CM5, CCSM4, and MRI–CGCM3 models relatively performed better with a consistent and accurate spatiotemporal simulation of Tmin, Tmax, and Tmean over SA. In terms of projected changes, Tmin, Tmax, and Tmean show a dominating and consistent warming pattern over SA with stronger intensity in higher latitude than mid–low latitudes under 1.5 oC, 2 oC, and 3 oC warming thresholds. The northwestern (eastern) regions of SA will witness greater (least) warming in Tmin, Tmax, and Tmean under all warming thresholds in RCP4.5 and RCP8.5 scenarios. Furthermore, the central and southern parts of SA will experience a moderate increase in Tmin, Tmax, and Tmean under all warming targets. The uneven and intensified patterns of Tmin, Tmax, and Tmean may result in temperature extremes, which would pose potential risks to the local population. Therefore, more attention should be paid on the regional and local perspectives to estimate the adverse impacts of these extremes under different global warming targets. We further suggest to project future changes in climate extremes over SA under different warming levels, which will be helpful in climate change adaptation and mitigation over the study region.