Uganda with its fragile ecosystem, large-scale human activities, and increasing population pressure, all of which combined, make this region increasingly susceptible to climate variation. This study examined the long-term trends of annual, seasonal, and monthly distributions of rainfall and temperature from 2001 to 2021 together with crop -wise agricultural productivity. For the analysis, we obtained CHIRPS -V2.0 (Climate Hazards Group InfraRed Precipitation with Station Data version 2.0) rainfall, Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), DMSP nighttime lights, ESA land cover attribution, and international crop production assessment records. Subsequently, several non -parametric statistical applications were applied to check the long-term spatio-temporal trends of climate parameters and their inter -relationship at higher significance using the Google Earth Engine platform. The investigation reveals an annual increase in LST, averaging 0.01 degree celsius/year along with decreasing rainfall (1.89 mm/year). However, regional climate trends are largely elevation -dependent, which are predominantly subjected to the northern part of the study area witnessing a slight decrease in LST and thereby increased rainfall. Moreover, the long-term spatial nexus estimation divulges a potent inverse association between rainfall and temperature in the north and northeastern regions of the study area. Concurrently, changing patterns also have led to a decline in crop production and deterioration in water availability, which is accompanied by various other abnormalities, including the scarcity of water resources and anthropogenic activities. Changing climate has had significant implications on crop production, largely on corn and soybean as long-term shifts influence it in average rainfall and temperature, yearly fluctuations, and disturbances during various growth stages.

Accurate estimates of extreme precipitation events play an important role in climate change studies and natural disaster risk assessments. This study aimed to evaluate the capability of the China Meteorological Forcing Dataset (CMFD), Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to detect the spatiotemporal patterns of extreme precipitation events over the Qinghai-Tibet Plateau (QTP) in China, from 1981 to 2014. Compared to the gauge-based precipitation dataset obtained from 101 stations across the region, 12 indices of extreme precipitation were employed and classified into three categories: fixed threshold, station-related threshold, and non-threshold indices. Correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), and Kling-Gupta efficiency (KGE), were used to assess the accuracy of extreme precipitation estimation; indices including probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were adopted to evaluate the ability of gridded products' to detect rain occurrences. The results indicated that all three gridded datasets showed acceptable representation of the extreme precipitation events over the QTP. CMFD and APHRODITE tended to slightly underestimate extreme precipitation indices (except for consecutive wet days), whereas CHIRPS overestimated most indices. Overall, CMFD outperformed the other datasets for capturing the spatiotemporal pattern of most extreme precipitation indices over the QTP. Although CHIRPS had lower levels of accuracy, the generated data had a higher spatial resolution, and with correction, it may be considered for small-scale studies in future research.