This study evaluates the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison Phase 6 (CMIP6). TaiESM1 is developed on the basis of the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The new additions include an improved triggering function in the cumulus convection scheme, a revised distribution‐based formula in the cloud fraction scheme, a new aerosol scheme, and a unique scheme for three‐dimensional surface absorption of shortwave radiation that accounts for the influence of complex terrains. In contrast to the majority of model evaluation processes, which focus mainly on the climatological mean, this evaluation focuses on climate variability parameters, including the diurnal rainfall cycle, precipitation extremes, synoptic eddy activity, intraseasonal fluctuation, monsoon evolution, and interannual and multidecadal atmospheric and oceanic teleconnection patterns. A series of intercomparisons between the simulations of TaiESM1 and CMIP6 models and observations indicate that TaiESM1, a participating model in CMIP6, can realistically simulate the observed climate variability at various time scales and are among the leading CMIP6 models in terms of many key climate features.

In boreal summer of 2022, Pakistan experienced extremely high rainfall, resulting in severe flooding and displacing over 30 million people. At the same time, heatwaves persisted over central China and Europe. The coexistence of these extreme events suggests a possible linkage. Our analysis indicated that the record rainfall was mainly induced by compounding factors. These included (1) La Niña-induced strong anomalous easterlies over the northern Indian subcontinent, (2) intense southerlies from the Arabian Sea with an upward trend in recent decades, (3) an interaction between extratropical and tropical systems, specifically the northerly flow downstream of the Europe blocking and the southerly monsoon flow from the Arabian Sea. Wave activity flux and regression analyses unveiled a distinct stationary Rossby wave-like pattern connecting the flooding in Pakistan and heatwaves in Europe and China. This pattern, an emerging teleconnection pattern in recent decade, exhibited substantial differences from the reported teleconnection patterns. We also noted the positive feedback of the excessive Pakistan rainfall could further enhance the large-scale background flow and the heavy rainfall itself. The 2022 Pakistan flood event was an intensified manifestation of the 2010 Pakistan flood event, which was also caused by compounding factors, but occurred in a more pronounced upward trend in the both tropics and extratropics.

The response of the Madden‐Julian oscillation (MJO) to ocean feedbacks is studied with coupled and uncoupled simulations of four general circulation models (GCMs). Monthly mean sea surface temperature (SST) from each coupled model is prescribed to its respective uncoupled simulation, to ensure identical SST mean‐state and low‐frequency variability between simulation pairs. Consistent with previous studies, coupling improves each model's ability to propagate MJO convection beyond the Maritime Continent. Analysis of the MJO moist static energy budget reveals that improved MJO eastward propagation in all four coupled models arises from enhanced meridional advection of column water vapor (CWV). Despite the identical mean‐state SST in each coupled and uncoupled simulation pair, coupling increases mean‐state CWV near the equator, sharpening equatorward moisture gradients and enhancing meridional moisture advection and MJO propagation. CWV composites during MJO and non‐MJO periods demonstrate that the MJO itself does not cause enhanced moisture gradients. Instead, analysis of low‐level subgrid‐scale moistening conditioned by rainfall rate (R) and SST anomaly reveals that coupling enhances low‐level convective moistening forR> 5 mm day⁻¹; this enhancement is most prominent near the equator. The low‐level moistening process varies among the four models, which we interpret in terms of their ocean model configurations, cumulus parameterizations, and sensitivities of convection to column relative humidity.

The transition to renewable energy sources, such as offshore wind farms, is essential in mitigating climate change. Taiwan has set ambitious targets to harness wind energy from the Taiwan Strait, but offshore wind farm installations are highly dependent on weather conditions, particularly wind speeds. This study examines the relationship between the El Niño-Southern Oscillation (ENSO) and offshore wind farm installation by assessing weather windows—periods with wind speeds below 12 m per second at a height of 100 m for at least 12 h. Our analysis shows that during La Niña years, the number of feasible weather windows decreases by up to 40 %, particularly between October and June, compared to neutral and El Niño years. This decrease can be as high as fourfold in December, significantly impacting installation schedules. Seasonal variations are also notable, with wind speeds exceeding 12 m s⁻¹ in winter 66.4 % of the time, compared to 29.4 % in spring, making spring and summer the most favorable periods for installation. However, even during these favorable seasons, La Niña years can bring higher wind speeds, necessitating careful planning. These results underscore the importance of integrating ENSO forecasts into project planning to avoid installation delays and optimize installation timelines. By leveraging seasonal and interannual climate variability predictions, decision-makers can improve the resilience of offshore wind farm projects and ensure efficient energy transition strategies.

A one-column, turbulent, and kinetic-energy-type ocean mixed-layer model (snow–ice–thermocline, SIT), when coupled with three atmospheric general circulation models (AGCMs), yields superior Madden–Julian oscillation (MJO) simulations. SIT is designed to have fine layers similar to those observed near the ocean surface; therefore, it can realistically simulate the diurnal warm layer and cool skin. This refined discretization of the near-surface ocean in SIT provides accurate sea surface temperature (SST) simulation, and thus facilitates realistic air–sea interaction. Coupling SIT with the European Centre/Hamburg Model version 5, the Community Atmosphere Model version 5, and the High-Resolution Atmospheric Model significantly improved MJO simulation in three coupled AGCMs compared to the AGCM driven by a prescribed SST. This study suggests two major improvements to the coupling process. First, during the preconditioning phase of MJO over the Maritime Continent (MC), the often underestimated surface latent heat bias in AGCMs can be corrected. Second, during the phase of strongest convection over the MC, the change in intraseasonal circulation in the meridional circulation enhancing near-surface moisture convergence is the dominant factor in the coupled simulations relative to the uncoupled experiments. The study results show that a fine vertical resolution near the surface, which better captures temperature variations in the upper few meters of the ocean, considerably improves different models with different configurations and physical parameterization schemes; this could be an essential factor for accurate MJO simulation.