In recent decades, extreme meteorological events have become more frequent and more severe. Flooding, heavy precipitation and droughts, in particular, are a few of these extreme events that can cause widespread property damage and loss of life. The climate is always changing and there is a general agreement that the changes will be more amplified and occur more rapidly due to anthropogenic influences. As a result, it is expected that the societal and economic impacts of heavy precipitation, floods, and droughts will increase as the climate continues to rapidly change. For these reasons, continued research to improve extreme precipitation predictions and long-term projections is vital. With improved projections, society will be able to improve their efforts to prepare for and implement better management practices to effectively adapt to the changing climate and help reduce the impacts of a changing climate. A great deal of progress has already been made in extreme precipitation research in relation to climate change. Overall, the tendency for dry areas to get drier and wet areas to get wetter has been identified. However, much of the work has focused on the daily timescale, and much less is known about sub-daily precipitation. It is becoming increasingly more important to consider this time scale because of evidence that climate change could have more of an impact on sub-daily (e.g., 3-hourly) rather than daily precipitation. To complicate the matter, there is still a need to evaluate the performance of global climate models in reproducing the precipitation statistics at the sub-daily time scales. The goal of this work is to evaluate the projected changes in precipitation at both the daily and sub-daily time scales and, more specifically, understand whether daily or sub-daily precipitation extremes will change more through the end of this century. However, to understand future projections it is first vital to analyze model accuracy and determine how well global climate models can reproduce the extreme precipitation statistics across the historical past. This is accomplished by comparing the historical runs for each model to observations during the same time period using several different methods, including a skill score analysis, using Taylor diagrams to visualize accuracy, and meridional plots that show intermodel variability. The results from this analysis show model performance for daily extreme precipitation is higher than that of the 3-hourly extreme precipitation. Although there are few models that do an adequate job of producing reliable results at the sub-daily time scale, there is an overall significant increase in skill as the temporal resolution becomes coarser. Variability also exists among models, with sub-daily precipitation having more widespread variability across every latitude, but daily precipitation has a wider range in potential extreme precipitation that is focused more in the tropics. Model performance also varies by season, resulting in higher performance and less variability among models for individual seasons. These results also point to several models that consistently perform well for both sub-daily and daily extreme precipitation, but it is still worth remembering that there is no guarantee that a good performance during the historical period ensures a good performance in the futures as well. The next part of the work focuses on the models with the highest performance in reproducing the observations. From there, it was possible to determine locations with the greatest changes in precipitation, the magnitude of changes, and whether sub-daily or daily extreme precipitation will be impacted more by climate change. Overall, extreme precipitation at both sub-daily and daily times scales is projected to increase globally. At the regional scale, precipitation is projected to primarily increase in the tropics, with smaller changes towards the poles. Areas of decreases in precipitation vary by model with the exception of a decrease in precipitation near the tropical Pacific Ocean that is seen in almost every model.

This research was aimed to evaluate the possibility of using satellite precipitation products (SPPs) and Numerical Weather Prediction (NWP) of precipitation for better hydrologic simulations and flood forecasting in the trans-boundary Chenab River Basin (CRB) in Pakistan. This research was divided into three parts. In the first part, two renowned SPPs, i.e., global precipitation mission (GPM) IMERG-F v6 and tropical rainfall measuring mission (TRMM) 3B42 v7, were incorporated in a semidistributed hydrological model, i.e., the soil and water assessment tool (SWAT), to assess the daily and monthly runoff pattern in Chenab River at the Marala Barrage gauging site in Pakistan. The results exhibit higher correlation between observed and simulated discharges at monthly timescale simulations rather than daily timescale simulations. Moreover, results show that IMERG-F is superior to 3B42 by indicating higher R2, higher Nash–Sutcliffe efficiency (NSE), and lower percent bias (PBIAS) at both monthly and daily timescale. In the second part, three latest half-hourly (HH) and daily (D) SPPs, i.e., 'IMERG-E', 'IMERGL', and 'IMERG-F', were evaluated for daily and monthly flow simulations in the SWAT model. The study revealed that monthly flow simulation performance is better than daily flow simulation in all sub-daily and daily SPPs-based models. Results depict that IMERGHHF and IMERG-DF yield the best performance among the other latency levels of SPPs. However, the IMERG-HHF based model has a reasonably higher daily correlation coefficient (R) and lower daily root mean square error (RMSE) than IMERG-DF. IMERG-HHF displays the lowest PBIAS for daily and monthly flow validations and it also represents relatively higher values of R2 and NSE than any other model for daily and monthly model validation. Moreover, the sub-daily IMERG based model outperformed the daily IMERG based model for all calibration and validation scenarios. IMERG-DL based model demonstrates poor performance among all of the SPPs, in daily and monthly flow validation, with low R2, low NSE, and high PBIAS. Additionally, the IMERG-HHE model outperformed IMERG-HHL. In the third and last part of this research, coupled hydro-meteorological precipitation information was used to forecast the 2016 flood event in the Chenab River Basin. The gaugecalibrated SPP, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model. This study revealed that the hydrologic simulations in IFAS, with global GFS forecasts, were unable to predict the flood peak for all lead times. Later, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. It was found in this study that the simulated hydrographs in the IFAS model, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R2, NSE and the lowest PBIAS compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.