Various state-of-the-art gridded satellite precipitation products (GPPs) have been derived from remote sensing and reanalysis data and are widely used in hydrological studies. An assessment of these GPPs against in-situ observations is necessary to determine their respective strengths and uncertainties. GPPs developed from satellite observations as a primary source were compared to in-situ observations, namely the Climate Hazard group Infrared Precipitation with Stations (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). These products were compared to in-situ data from 51 stations, spanning 1998-2016, across Pakistan on daily, monthly, annual and interannual time scales. Spatiotemporal climatology was well captured by all products, with more precipitation in the north eastern parts during the monsoon months and vice-versa. Daily precipitation with amount larger than 10 mm showed significant (95%, Kolmogorov-Smirnov test) agreement with the in-situ data, especially TMPA, followed by CHIRPS and MSWEP. At monthly scales, there were significant correlations (R) between the GPPs and in-situ records, suggesting similar dynamics; however, statistical metrics suggested that the performance of these products varies from north towards south. Temporal agreement on an interannual scale was higher in the central and southern parts which followed precipitation seasonality. TMPA performed the best, followed in order by CHIRPS, MSWEP and PERSIANN-CDR.

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Controversial results in the drying and wetting trends were found with different indices and potential evapotranspiration calculations in previous studies. Here we make an attempt to find robust conclusions of drying and wetting trends over regions by coherent results of various independent indices by using China (1961–2012) as a study area. Precipitation, statistical, and physical drought indices, including the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) and self‐calibrating PDSI (sc_PDSI), with both the Penman‐Monteith (PM) and Thornthwaite (TH) approaches in PDSI calculation are considered. In consequence, four PDSI variants of PDSI_pm, sc_PDSI_pm, PDSI_th, and sc_PDSI_th are involved. To illustrate regional characteristics, six climatic regions based on the Köppen climate classification are defined. At the national scale, precipitation and SPEI indicate wetting trends but all PDSI variants have drying trends. On the other hand, these six indices exhibit coherent results in five of these six regions. Increases in wetness occur in arid region and the Qinghai‐Tibet Plateau. Drying trends were found in semiarid and cold and temperate semihumid regions. Only the humid region in southeastern China is seen to have increasing precipitation and SPEI and decreasing PDSI variants. From the perspective of climatic regions, the drying trends mainly occur in the transition regions between the humid and arid regions in China. The spatial pattern of changes in droughts could be categorized by climatic zones, and the changes at regional scale are robust based on these six indices.

Assessing the long-term precipitation changes is of utmost importance for understanding the impact of climate change. This study investigated the variability of extreme precipitation events over Pakistan on the basis of daily precipitation data from 51 weather stations from 1980-2016. The non-parametric Mann–Kendall, Sen’s slope estimator, least squares method, and two-tailed simple t-test methods were used to assess the trend in eight precipitation extreme indices. These indices were wet days (R1 ≥1 mm), heavy precipitation days (R10 ≥ 10 mm), very heavy precipitation days (R20 ≥ 20 mm), severe precipitation (R50 ≥ 50 mm), very wet days (R95p) defining daily precipitation ≥ 95 percentile, extremely wet days (R99p) defining daily precipitation ≥ 99 percentile, annual total precipitation in wet days (PRCPTOT), and mean precipitation amount on wet days as simple daily intensity index (SDII). The study is unique in terms of using high stations’ density, extended temporal coverage, advanced statistical techniques, and additional extreme indices. Furthermore, this study is the first of its kind to detect abrupt changes in the temporal trend of precipitation extremes over Pakistan. The results showed that the spatial distribution of trends in different precipitation extreme indices over the study region increased as a whole; however, the monsoon and westerlies humid regions experienced a decreasing trend of extreme precipitation indices during the study period. The results of the sequential Mann–Kendall (SqMK) test showed that all precipitation extremes exhibited abrupt dynamic changes in temporal trend during the study period; however, the most frequent mutation points with increasing tendency were observed during 2011 and onward. The results further illustrated that the linear trend of all extreme indices showed an increasing tendency from 1980- 2016. Similarly, for elevation, most of the precipitation extremes showed an inverse relationship, suggesting a decrease of precipitation along the latitudinal extent of the country. The spatiotemporal variations in precipitation extremes give a possible indication of the ongoing phenomena of climate change and variability that modified the precipitation regime of Pakistan. On the basis of the current findings, the study recommends that future studies focus on underlying physical and natural drivers of precipitation variability over the study region.

Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations and reanalysis data. In this study, we first investigate the long-term changes of LST during 2003–2017 on a per-pixel basis using three different datasets derived from (i) the Atmospheric Infrared Sounder (AIRS) onboard Aqua satellite, (ii) the Moderate Resolution Imaging Spectroradiometer (MODIS) also aboard Aqua, and (iii) the recently released ERA5-Land reanalysis data. It was found that the spatio-temporal patterns of these data agree very well. All three products globally showed an uptrend in the annual average LST during 2003–2017 but with considerable spatial variations. The strongest increase was found over the region north of 45° N, particularly over Asian Russia, whereas a slight decrease was observed over Australia. The regression analysis indicated that precipitation (P), incoming surface solar radiation (SW↓), and incoming surface longwave radiation (LW↓) can together explain the inter-annual LST variations over most regions, except over tropical forests, where the inter-annual LST variation is low. Spatially, the LST changes during 2003–2017 over the region north of 45° N were mainly influenced by LW↓, while P and SW↓ played a more important role over other regions. A detailed look at Asian Russia and the Amazon rainforest at a monthly time scale showed that warming in Asian Russia is dominated by LST increases in February–April, which are closely related with the simultaneously increasing LW↓ and clouds. Over the southern Amazon, the most apparent LST increase is found in the dry season (August–September), primarily affected by decreasing P. In addition, increasing SW↓ associated with decreasing atmospheric aerosols was another factor found to cause LST increases. This study shows a high level of consistency among LST trends derived from satellite and reanalysis products, thus providing more robust characteristics of the spatio-temporal LST changes during 2003–2017. Furthermore, the major climatic drivers of LST changes during 2003–2017 were identified over different regions, which might help us predict the LST in response to changing climate in the future.

Understanding the Tibetan plateau (TP) thermal processes is of utmost significance in changing climate. This study investigates the effect of soil moisture in changing the TP thermal profile and consequently summer precipitation in South Asia (SA). Soil moisture from Special Sensor Microwave Imager (SSMI) developed from the F08, F11, and F13 fundamental climate data record and atmospheric reanalysis from ERA-Interim, MERRA-2, and NCEP/CFSR during 1988-2008 are used. A generalized linear method that assesses the reciprocal forcing between two connected fields named Coupled Manifold Technique (CMT) is applied to TP soil moisture and SA summer precipitation. It is revealed that interannual variations of SA precipitation are significantly (confidence level= 99%) impacted by TP soil moisture and explained ratio of variance in SA is 0.3-0.4. Composite analysis indicates that SA summer precipitation has positive anomalies in response to dry TP soil moisture in the previous spring and vice versa. For understanding the possible mechanism, thermal processes, relative humidity, wind components, and moisture flux anomalies were calculated for dry/wet TP soil moisture and summer precipitation. The results suggested that TP soil moisture is likely to regulate near-surface energy balance and diabatic heating profile over TP. As a result, the surrounding lower level westerlies/easterlies (850 hPa) converge/diverge, which are associated with divergence/convergence at the upper troposphere (200 hPa). The westerlies/easterlies are usually moisture-rich/deficient and thus cause more/less precipitation in western/eastern SA. It is thus suggested that the spring soil moisture may affect the thermal profile of TP, affecting the summer precipitation in SA as a consequence.

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