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A new algorithm is proposed to estimate the cooling amplitude over North China induced by each Ural blocking event. Taking full account of potential transient temperature rises during the cooling process and the lag time of cooling relative to the blocking, this algorithm provides more detailed analysis which should not be possible by using former methods. The amplitude of the Ural blocking-related cooling events is found to have a broad distribution. Further, although most Ural blocking events lead to severe cold surges over North China, the number of Ural blocking events that do not induce significant cooling over North China cannot be ignored. The possible reasons for the wide range in cooling amplitude are explored in terms of the lifetimes and geographical centers of the blocking highs, the circulation patterns preceding the onset of the cooling events, and the snowfall associated with cooling events. Larger amplitude cooling events occur in Ural blocking highs that have longer lifetimes and northwestward displacements of their geographical centers. The northward displacement of a Ural blocking center favors the advection of extremely cold air from the Arctic regions, which accumulates in West Siberia and subsequently gives rise to the most severe cold surges over North China. The lack of activities of cold surges before the blocking-related cooling events not only amplifies the cooling amplitude directly, but also increases the occurrence probabilities of snowfalls through its modulation on the local specific humidity. The increased albedo and subsequent snow-melt induced cooling further amplify the cooling amplitude.

Most of the coupled models contain a strong warm bias in sea surface temperature (SST) over the Atlantic Cold Tongue (ACT) region (10° S–3° N, 20° W–10° E) during June–August (JJA) and September–November (SON). In this study, the origins of the ACT SST bias and their relative contributions to the bias are explored by conducting a set of sensitivity experiments, which are based on an ocean-ice model, and by ignoring the nonlinear effects of each origin. The origins for the warm bias over the ACT in the coupled climate model during JJA are estimated as follows: westerly wind bias along the equator (5° S–5° N) during March–May (MAM; contributes approximately 32.6% of the warm bias), northerly bias over the southern tropical Atlantic (25° S–3° N, 40° W–20° E) during MAM and JJA (21.4%), bias in the surface specific humidity and surface air temperature (11.9%), and downward shortwave radiation bias (6.5%). The origins of the ACT bias during SON are as follows: northerly bias over the southern tropical Atlantic during SON (31.2%), bias in the surface specific humidity and surface air temperature (27.9%), downward shortwave radiation bias (17.4%), and zonal wind bias (13.4%). Note that these contribution ratios of these origins may be model-dependent. In addition, the local and non-local effects of the zonal wind bias are explored explicitly, while those of all the other biases are examined implicitly. Therefore, a better-performing atmospheric component is crucial when simulating zonal winds during MAM along the equator (5° S–5° N) and meridional winds during MAM, JJA, and SON over the southern tropical Atlantic, which will alleviate the warm bias over the ACT region in the coupled climate model.

Nine precipitation regimes over South China are obtained by applying the Self-Organizing Map (SOM) technique to the sub-daily precipitation during the pre-monsoon season (April to June) of 1979–2015. These nine regimes are distinct from each other in terms of precipitation amount and spatial pattern. The relationships between precipitation and different atmospheric dynamic and thermodynamic factors (large-scale divergence, water vapor flux, low-level jet, precipitable water, convective available potential energy (CAPE), and K index) are explored under the nine regimes. The upper-level divergence performs best in indicating the geographic positions of precipitation centers, which are also modulated by the orientations of low-level jets. The estimation of water vapor transport reveals that there are two major moisture sources for the precipitation during the pre-monsoon season, i.e., the Bay of Bengal (for all the nine regimes) and the South China Sea and West Pacific Ocean (for five regimes). Furthermore, the occurrence probability of more precipitation increases with the water vapor transported from the South China Sea and West Pacific Ocean. Compared to CAPE, K index performs better in indicating the precipitation centers and has a tighter relationship with area-average precipitation. The precipitable water exhibits complicated relationships with spatial patterns and amounts of precipitation, indicating that it may be not a good indicator for precipitation during pre-monsoon season over South China. Estimation of the persistence and transformation probabilities for precipitation regimes reveals that the persistence probabilities basically decrease with the precipitation amounts, and the transformations between different precipitation regimes are inclined to be associated with the southward shifts of precipitation centers.