This study aims to assess the accuracy of two satellite‐based precipitation products (SBPPs), that is, Tropical Rainfall Measurement Mission (TRMM)‐based Multi‐satellite Precipitation Analysis (TMPA) and its upgraded version Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement (IMERG), in capturing spatial and temporal variation of precipitation and their application for extreme events (high‐intensity precipitation and drought). They were evaluated against 142‐gauge stations from Nepal during 2001–2018. The results show that, in general, both SBPPs show the overall characteristics of precipitation patterns, although underestimated the mean annual precipitation during the study period. It was also noted that IMERG product yields better performance to detect precipitation events (probability of detection) and no‐precipitation events (false alarm ratio) than TMPA. Based on four different extreme precipitation indices: heavy precipitation events (R10mm), extreme precipitation events (R25mm), five consecutive dry days (CDD), and five consecutive wet days (CWD), it was observed that the SBPPs underestimated the frequency of R25mm and CDD spells while overestimated R10mm and CWD spells. Additionally, both SBPPs exhibited considerable capabilities in capturing the drought events during the study period. Overall, the drought event, bias, and frequency show that the IMERG product has slightly better capabilities to capture drought than the TMPA product. In general, IMERG was found to be superior at a daily timescale, while TMPA shows consistent performance on a monthly scale during the study period. Furthermore, there is still space for further improvement of IMERG rainfall retrieval algorithms.

Precipitation is the most important variable in the climate system and the dominant driver of land surface hydrologic conditions. Rain gauge measurement provides precipitation estimates on the ground surface; however, these measurements are sparse, especially in the high-elevation areas of Nepal. Reanalysis datasets are the potential alternative for precipitation measurement, although it must be evaluated and validated before use. This study evaluates the performance of second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) datasets with the 141-gauge observations from Nepal between 2000 and 2018 on monthly, seasonal, and annual timescales. Different statistical measures based on the Correlation Coefficient (R), Mean Bias (MB), Root-Mean-Square Error (RMSE), and Nash–Sutcliffe efficiency (NSE) were adopted to determine the performance of both MERRA-2 datasets. The results revealed that gauge calibrated (MERRA-C) underestimated, whereas model-only (MERRA-NC) overestimated the observed seasonal cycle of precipitation. However, both datasets were able to reproduce seasonal precipitation cycle with a high correlation (R ≥ 0.95), as revealed by observation. MERRA-C datasets showed a more consistent spatial performance (higher R-value) to the observed datasets than MERRA-NC, while MERRA-NC is more reasonable to estimate precipitation amount (lower MB) across the country. Both MERRA-2 datasets performed better in winter, post-monsoon, and pre-monsoon than in summer monsoon. Moreover, MERRA-NC overestimated the observed precipitation in mid and high-elevation areas, whereas MERRA-C severely underestimated at most of the stations throughout all seasons. Among both datasets, MERRA-C was only able to reproduce the observed elevation dependency pattern. Furthermore, uncertainties in MERRA-2 precipitation products mentioned above are still worthy of attention by data developers and users.

Drought influences agriculture, hydrology, ecology and socio‐economic systems globally. As agriculture is the primary source for livelihoods and contributes to ~27% of Nepalʼs total gross domestic product, it is essential to understand the impact of drought on maize and wheat crop yields to minimize the drought‐related risks. This study presents insights about agricultural drought across Nepal during 1987–2017 using the Standardized Precipitation Evapotranspiration Index (SPEI). The temporal evolution of SPEI time series has revealed frequent occurrences of drought episodes during the cropping cycle of summer maize and winter wheat crops. Moreover, the turning point of the drought was detected in 2000 (1987–2000, 2001–2017) in different regions. The averaged frequency for the SPEIs (1, 3, 6 and 12) of drought years for summer maize (winter wheat) in the western, central and eastern regions increased by 13% (12.5%), 6% (7.5%) and 7% (8%), respectively, from 1987–2000 to 2001–2017. The relationship between Standardized Yield Residual Series, the detrended SPEI at 1–12 lags and soil moisture was observed for both crops. The most correlated crop growth period for summer maize and winter wheat was the sowing and growing period, respectively, indicating the sensitive period of water deficit. Besides, the correlation performed in the two sub‐periods (1987–2000 and 2001–2017) shows that drought impacts increased in the western and central regions, whereas they substantially decreased in the eastern region during the cropping period of summer maize. However, the drought sensitivity for winter wheat was decreased in the western region but significantly increased in the central and eastern regions of Nepal. The results of this study provide important information useful for policymakers in monitoring and mitigating the drought‐related risks on maize and wheat crops in Nepal.

The Diurnal Temperature Range (DTR) profoundly affects human health, agriculture, eco-system, and socioeconomic systems. In this study, we analyzed past and future changes in DTR using gridded Climate Research Unit (CRU) datasets for the years 1950–2020 and an ensemble means of thirteen bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) models under different Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) scenarios for the rest of the 21st century over the southern slope of Central Himalaya, Nepal. Furthermore, the potential drivers (precipitation and cloud cover) of seasonal and annual DTR were studied using correlation analysis. This study found that the DTR trends generally declined; the highest decrease was observed in the pre-monsoon and winter at a rate of 0.09 °C/decade (p ≤ 0.01). As expected, DTR demonstrated a significant negative correlation with cloudiness and precipitation in all four seasons. Further, the decreased DTR was weakly related to the Sea Surface Temperature variation (SST) in the tropical Pacific and Indian Oceans. We found that the projected DTR changes in the future varied from a marginal increase under the SSP1-2.6 (only pre-monsoon) scenario to continued significant decreases under SSP2-4.5 and SSP5-8.5. Insights based on retrospective and prospective evaluation help to understand the long-term evolution of diurnal temperature variations.

Nepal is highly vulnerable to climate change with increased fire occurrences and fire burned areas in recent years; therefore, we accessed the climatic drivers for its variability using fire burned areas product of Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 and 2020. The peak fire burned areas were observed in the spring season (~91%) from March to May, especially higher in the lowlands of the western and central parts. At the interannual timescale, low precipitation, humidity, soil moisture, and high temperature supported the existence of spring fire. Combining these factors induces drought conditions, enhancing evapotranspiration from vegetation and providing more combustible fuels. Furthermore, the El Niño phase in the central‐eastern Pacific Ocean is related to the weakened westerly moisture transport and moisture divergence that creates dry and warm conditions leading to increased fire activities. Thus, this study could be helpful for preparedness, management, and policy‐making to limit the multi‐dimensional losses in the ecosystem and society due to fire.