Precipitation measurements in the Mekong River Basin (MRB) are full of variability due to this domain's varied weather systems, climate conditions, elevation, and specific land–atmosphere interactions. This study provides an in-depth evaluation of the differences between four gridded precipitation products [i.e. Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Tropical Rainfall Measuring Mission (TRMM), CPC MORPHing technique (CMORPH), and Climatic Research Unit (CRU)] over the Greater Mekong Subregion. Precipitation data from a total of 242 stations in this domain are used to investigate the quality of the gridded products. Our analysis verifies that gauge-based APHRODITE exhibits the highest correlations with the station data as well as the highest probability of detection of daily precipitation. The false alarm ratio, on the other hand, is slightly in favour of TRMM and CMORPH. Subtracting APHRODITE (as baseline) from TRMM and CMORPH reveals the spatial and frequency distribution of potential biases. The results indicate that TRMM appears to have a wet bias in most areas, while CMORPH shows no similar or consistent bias over APHRODITE. To utilize the higher accuracy of APHRODITE and the finer spatial and temporal footprints of CMORPH, a new restructuring algorithm is introduced in this study. The algorithm is capable of eliminating biases and possible artefacts associated with CMORPH while resolving the resolution discrepancies between the two data sets.

The detrimental impacts of climate variability on water, agriculture, and food resources in East Africa underscore the importance of reliable seasonal climate predictions. This study compares several forecasting methods using sea surface temperature (SST) anomalies to predict East African rains with various lead times. It is shown that the forecasts can explain more than 50% of the short rains (boreal autumn) variance across large regions of Ethiopia, Somalia, and Kenya, and reaffirm the importance of the Walker‐like circulation over the Indian Ocean. The forecasts also explain more than 50% of the long rains (boreal spring) over Uganda, Lake Victoria, and western Kenya, and demonstrate the strong connection of the long rains to the SSTs in the southern Atlantic Ocean, Mediterranean Sea, southwestern Indian Ocean, and Arabian Sea. For the unimodal rains in Tanzania, the forecasts explain more than 40% of the rainfall variance over northwestern Tanzania based on SST predictors in the southwestern Indian Ocean and the subtropical North Atlantic. For the South Sudan and Ethiopian Kiremt rains, the forecast skill is less pronounced. The predictor regions (dipoles) for a recently developed method are validated through composite maps of surface and atmospheric reanalysis variables. Lastly, 2010–2011 forecasts for both the long and short rains demonstrate skill in predicting the recent drought suggesting that seasonal climate forecasts can inform natural disaster preparedness and water resources planning in East Africa.

Land use/cover change (LUCC) has taken place since the 1990s in central Taiwan; however, its impacts on the local and regional hydroclimatology are not understood thoroughly. This study is grounded in a numerical experiment using the Weather Research and Forecasting (WRF) model and statistical assessments of continuous land cover and gridded precipitation data derived for central Taiwan. We incorporate survey-based land use data in 1995 and 2007 in driving WRF to simulate selective non-rainy and rainy (dry and wet) cases under weak synoptic forcings in July and August (JA). The two land-use conditions reveal changes in simulation fields on account of increased urban and built-up lands. Results averaged over the dry cases show increased (diminished) sensible heat fluxes and 2 m temperatures (latent heat fluxes and 2 m specific humidity) in 2007 compared to that in 1995. The wet-case simulation further identifies intensified precipitation over the downwind areas of urban and built-up lands, strongly subject to local topography and prevailing winds. Statistical assessments of the Landsat land cover and gridded precipitation data verify significant increasing trends in urbanization and the JA rainfall. Regression-based analysis that scales the effect of the LUCC on the change in precipitation corroborates the WRF simulation: LUCC has induced eastward, downwind association with the JA rainfall.