An extra-large subsurface anticyclonic eddy (SAE) with horizontal scale of 470 km was detected in the northwestern Pacific subtropical gyre by in situ measurements in October 2014. The SAE exhibited a lens-shaped vertical structure with shoaling of the seasonal thermocline and deepening of the main thermocline. Consequently, the water in the eddy core was colder above 200 m and warmer below 200 m than the surrounding waters with maximum temperature anomalies of -1.2°C and 3.5°C located at ~100 m and ~450 m depths, respectively. The central water mass of the SAE was characterized as low potential vorticity water, i.e., the north Pacific Subtropical Mode Water (STMW). Swirl velocity of the SAE was directly observed by ship-mounted ADCP (Acoustic Doppler Current Profilers). The maximum azimuthal velocity reached 0.35 ms -1 near a 110 km radius at ~ 200 m depth, which was comparable with the maximum velocity of the northward Kuroshio east of Taiwan at the same depth. Threedimensional structure and evolutionary process of the SAE were also presented using Argo float profile data as well as the satellite altimeter data. The results indicated that the SAE was generated in the region of the STMW in February, then propagated westward over 1500 km at a mean speed of ~0.06 ms -1 and finally disappeared east of Taiwan in December, transporting ~0.5 Sv (Sv=10 6 m 3 s -1 ) STMW..

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Knowledge of spatial correlations of precipitation is important for the generation of grid-based surface precipitation data sets, deployment of data collection, selection of downscaling strategies, and interpretation of paleoclimate reconstructions. Spatial correlations of daily precipitation in China were analyzed based on a daily precipitation data set from 1951 through 2014 for 2,208 stations by dividing them into 13 regions. Interstation Pearson correlation coefficient r for the daily precipitation series and the corresponding interstation distance d were calculated for each region. The exponential spatial correlationwas fitted by the r-d pairs, in which c 0 , d 0 and s 0 were the parameter variance, scale and shape, respectively. The results showed that: (a) The determination coefficient R 2 of the correlation model varied from 0.54 to 0.96, with a mean of 0.82 and the regional maximum correlation distance d 0 varied from 102.2 to 201.7 km, with a mean of 155.2 km. Western regions generally had smaller d 0 than eastern regions, which indicates rain events in the western regions were more local; (b) The goodness-of-fit of the model was improved by dividing samples into West-East (W-E) and North-South (N-S) directions. The average of d 0 for all regions(190.2 km) for the W-E direction is larger than that for the N-S direction (142.9 km); (c) The correlation distances in summer and dry years are shorter than those in winter and wet years. However, the difference of correlation distance between dry and wet years was subtle compared with those between summer and winter, and between W-E and N-S directions. Seven regions were divided based on the spatial correlations of daily precipitation and different spatial models were suggested to be used for different regions, seasons and directions when the interpolation of daily precipitation is conducted for the generation of gridded surface precipitation data sets.

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