This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates （SEs）, and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps. First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions （MPs） over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation （OI）-based merging scheme （referred as the HL-OI scheme） is used to combine unbiased gahge observations, SEs, and MPs to reduce random error from each source and to produce a gauge--satellite-model merged daily precipitation analysis, called BMEP-d （Beijing Climate Center Merged Estimation of Precipitation with daily resolution）, with complete global coverage. The BMEP-d data from a four-year period （2011- 14） demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality. Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD （GPCP one-degree daily） show that the consistencies between B MEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.
Five sets of model sensitivity experiments are conducted to investigate the influence of tropical cyclone （TC） genesis location and atmospheric circulation on interannual variability of TC intensity in the western North Pacific （WNP）. In each experiment, bogus TCs are placed at different initial locations, and simulations are conducted with identical initial and boundary conditions. In the first three experiments, the specified atmospheric and SST conditions represent the mean conditions of E1 Nifio, La Nifia, and neutral years. The other two experiments are conducted with the specified atmospheric conditions of E1 Nifio and La Nifia years but with SSTs exchanged. The model results suggest that TCs generated in the southeastern WNP incurred more favorable environmental conditions for development than TCs generated elsewhere. The different TC intensities between E1 Nifio and La Nifia years are caused by difference in TC genesis location and low-level vorticity （VOR）. VOR plays a significant role in the intensities of TCs with the same genesis locations between E1 Nifio and La Nina years.
