## 5 Discussion
The results are well associated with the findings of [27], who reported the better pattern similarity performance of TRMM product in terms of higher CC of around 80% against other datasets during annual time scale over the upper Indus basin. The spatial distribution of statistical metrics (ME, RMSD, and CC) indicates the better performance of GPCC and TRMM products. However, the range of deviation in GB and SB products were more obvious in the northern Punjab with the overall pattern of underestimation (GB products) and overestimation (SB products) from the north to south gradient. The possible explanation of estimated deviation in GB and SB products over the northern Punjab could be due to wind induced errors and the presence of higher amount of aerosols in the atmosphere. The higher amount of aerosols in the atmosphere intercepts with the precipitation that reduces the efficiency of ground based meteorological stations [80]. Moreover, the wind induced errors can be attributed to the efficacy of ground based stations [81] Similarly, the pattern of overestimation in SB products particularly over northern Punjab could be associated with the higher rate of evaporation in the lower reaches of the mountains. These results are well associated with the findings of [82], who indicated the overestimation of SB products in the foothills of Himalaya mountain ranges over northern Pakistan. Moreover, the deviation in accuracy of GB products over northern Punjab could be due to inter-annual variability in weather, orographic effects and fewer gauges over the mountainous region. Many studies reported such effects in different climate studies over the northern belt of Pakistan [27; 28; 79]. Moreover, the fewer pixels of precipitation products also showed a weaker ability to capture the accurate spatial pattern in the central and southern parts of the study area. The possible explanation of accuracy deviation in these areas could be due the rapid extent of urban areas. Most of the meteorological station installed in the non-urban domain which could not represent the exact situation of climate variability in urban areas. Several studies documented the effect of urbanization on climate variability at a spatial scale [83]. The MK trends of observed precipitation indicate insignificant increasing trends during the period 1979-2017 and 2003-2017 with the rate of 1.12 and 5.5 mm/decade, respectively. The increasing trend was faster during period 2003-17 as compared with the period 1979-2017. The MK trend of GB and SB products showed the increasing trend with different magnitude of overestimation, except UDel and CPC products, which indicates decreasing trend as compared with observed precipitation. Furthermore, the abrupt transition analysis indicates the multiple turning points in precipitation data series during the period 1980-2005 with major abrupt changes in progressive series were detected during the period of 1984-1985 (negative), 1988-1989 (positive), 1998-1999 (negative), and 2005-2006 (positive). Many studies over the Asian and Indian monsoon system exhibited similar kind of precipitation trends over different timescales. [84; 85; 86]. The negative precipitation trend detected during the period of 1984-1985 and 1998-1999 could be due to major drought period over the whole country. The results are consistent with the findings of [87; 88], who reported the severe drought conditions in the country during the mid-80s and late 90s. Moreover, the gentle increase with no obvious break point has been detected in progressive data series during the period of 2005-2017. The large inter-annual variability in reference data series were well captured by GPCC and TRMM products during the period 1979-2017 and 2003-2017, respectively. In spite of the fact that the spatial and temporal performance of GPCC and TRMM products outperformed the respective products in terms of lower values of ME, RMSD, and higher range of CC against in-situ records during the period 1979-2017 and 2003-2017, respectively.