Current and future changes in extreme precipitation events (EPEs) and atmospheric heatwaves (AHWs) are studied based on precipitation and near-surface maximum temperature (Tasmax) data obtained from observations and the Coupled Model Intercomparison Project Phase 6. Further, the linkage of such events with sea surface temperature anomalies (SSTAs), tropical cyclones, and climate indices are explored. The results indicate that EPEs and AHWs are becoming increasingly frequent in Eastern China, particularly in the southern, southwest, and southeast coast. By comparing the Shared Socioeconomic Pathway (SSP) 585 and SSP245, we hypotheses that the increased three-fold of AHW days may compresses the precipitation time-window, the number of EPEs and amount of extreme precipitation (AEP) has increased. SSTA variability in the Indian Ocean (IO) and Tropical North Atlantic (TNA) suggests a significant positive correlation with precipitation in Southern China and the southeastern coast. The SSTA variability over the Western Pacific (WP), IO, and TNA has a positive anomalous influence on Tasmax in most areas of Eastern China. The intensification and slow decay of land falling tropical cyclones are also contribute to EPEs. The responses of precipitation and Tasmax to the WP subtropical high, Pacific Decadal Oscillation, IO Dipole, and North Atlantic Oscillation vary by region, and the impacts of these climate indices on Tasmax are opposite to those on precipitation. The WP subtropical high and IO Dipole play a critical role in positive precipitation and Tasmax anomalies.

Due to considerable social and economic implications, there is a continuously increasing demand for skillful subseasonal-to-seasonal forecasts of weather and climate extremes. Atmospheric rivers (ARs) characterized by intense lower tropospheric plumes of moisture transport are essential to midlatitude extreme precipitation. This study aims to provide a global evaluation of subseasonal prediction skill of ARs out to a 4-week lead based on a 10-member 20-year hindcast experiment using the recently developed Seamless System for Prediction and Earth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL). We apply an aggregate measure to quantify the prediction skill by counting AR occurrence days within a week-long period (from AR-week1 to AR-week4). Reliable subseasonal forecast skill of ARs is detected up to 3 weeks lead over the subtropical to midlatitude North Pacific and North Atlantic (winter) and East Asia (summer), with slightly higher forecast skill in winter compared with summer seasons. The SPEAR forecast model shows systematic negative bias over East Asia, Gulf Stream, and surrounding regions relative to the ERA5 reanalysis, especially during summer seasons. We further compare the SPEAR model's overall subseasonal AR forecast skill with several other forecast models such as ECMWF and NCEP. Besides, the reliability of the SPEAR model in forecasting subseasonal ARs is examined from the perspective of the so-called "signal-to-noise paradox," which implies whether a specific model is overestimating or underestimating the prediction skill. The potential impacts of the El Niño–Southern Oscillation and Madden–Julian Oscillation on the magnitude and subseasonal AR prediction skill are discussed with a specific focus on western North America.

Due to considerable social and economic implications, there is a continuously increasing demand for decadal climate predictions. However, decadal predictions remain a challenging problem largely owing to the insufficient knowledge of decadal predictability. The overarching goal of this work is to understand decadal climate predictability in the global ocean. Specifically, this work is motivated by current challenges in decadal predictability and has three major objectives.

The first objective is to investigate the limits and mechanisms of decadal predictability, particularly the unresolved role of internal atmospheric noise in decadal predictability. The interactive ensemble (IE) coupling technique is used to quantify how the internal atmospheric noise at the air-sea interface impacts decadal predictability. We apply the nonlinear local Lyapunov exponent method to the Community Climate System Model comparing control simulations with IE simulations. The global patterns of decadal predictability are shown for both models and observations and we find that the impact of internal atmospheric noise on decadal predictability is not a linear question and largely dependent on the background coupling and dynamics.

The second objective is to address the so-called “signal-to-noise paradox”. The essence of the paradox is that the signal-to-noise ratio in models can be unrealistically too small and models seem to make better predictions of the observations than they predict themselves. We introduce a Markov model framework to represent the ensemble forecasts and reproduce the paradox, which is primarily dependent on the magnitude of the persistence and noise variance between models and observations. The Markov model framework is applied to the North Atlantic Oscillation index based on the coupled models from the fifth Coupled Model Intercomparison Project (CMIP5). The results suggest the widespread existence of the signal-to-noise paradox that may exist at different timescales.

We re-examine decadal predictability from the lens of the signal-to-noise paradox in the context of CMIP5 models for the sea surface temperature and sea level pressure fields. We demonstrate that decadal predictability is generally underestimated in CMIP5 models, which is closely related to the paradox. Models are likely to underestimate decadal predictability in regions where it is likely to have the paradox.

The third objective is to determine if this underestimate of decadal predictability is, at least partially, due to missing ocean mesoscale processes and features in CMIP5 models. A suite of coupled model experiments is performed with an eddy-resolving and eddy- parameterized ocean component. Again, the results are discussed through the lens of the signal-to-noise paradox. Compared with eddy-parameterized models, less chance of existence for the paradox is seen in eddy-resolving models, particularly over eddy-rich regions, where increased decadal predictability is also identified. This enhanced predictability is possible due to the enhanced vertical connectivity, which is demonstrated through ocean vertical structure and the relationships between the deep ocean and surface processes. We argue that the presence of mesoscale ocean features and associated vertical connectivity significantly influence decadal variability, predictability, and the signal-to- noise paradox.

Overall, this work summarizes major challenges facing decadal predictability and aims to understand decadal predictability from the perspectives of the internal atmospheric dynamics, signal-to-noise paradox, and ocean mesoscale features. These findings not only suggest potential opportunities to advance decadal climate predictability in future studies, but also provide guidance on future model development and initialization.

Decadal climate predictability has received considerable scientific interest in recent years, yet the limits and mechanisms for decadal predictability are currently not well known. It is widely accepted that noise due to internal atmospheric dynamics at the air‐sea interface influences predictability. The purpose of this paper is to use the interactive ensemble (IE) coupling strategy to quantify how internal atmospheric noise at the air‐sea interface impacts decadal predictability. The IE technique can significantly reduce internal atmospheric noise and has proven useful in assessing seasonal‐to‐interannual variability and predictability. Here we focus on decadal timescales and apply the nonlinear local Lyapunov exponent method to the Community Climate System Model comparing control simulations with IE simulations. This is the first time the nonlinear local Lyapunov exponent has been applied to the state‐of‐the‐art coupled models. The global patterns of decadal predictability are discussed from the perspective of internal atmospheric noise and ocean dynamics.