Marine cold‐spell (MCS) metrics—such as frequency and intensity—are decreasing globally, while marine heatwave (MHW) metrics are increasing due to sea surface temperature (SST) warming. However, the concomitant changes in MHW and MCS metrics, and whether SST warming can similarly explain the decreasing MCS metrics remain unclear. Here, we provide a comparative global assessment of these changes based on satellite SST observations over 1982–2020. Across the globe, we find distinct differences in mean MHW and MCS metrics. Furthermore, decreasing trends in MCS metrics are not necessarily aligned with increasing trends in MHW metrics. While differences in intensity trends are mainly explained by SST variance trends, differences in trends of annual days are less clear. Overall, decreasing MCS days and intensities are found to be largely driven by warming SST, rather than SST variance changes. Therefore, it is expected that MCS days and intensity will continue diminishing under global warming.

Numerous recent studies have focused on the devastating effects of discrete and prolonged extreme oceanic warm water events, also known as 'marine heatwaves' (MHWs). Such events cause devastating impacts to marine biodiversity (e.g.,

Marine heatwaves (MHWs) off Western Australia (110°E–116°E, 22°S–32°S; hereafter, WA MHWs) can cause devastating ecological impacts, as was evidenced by the 2011 extreme event. Previous studies suggest that La Niña is the major large-scale driver of WA MHWs, while Indian Ocean Dipole (IOD) may also play a role. Here, we investigate historical WA MHWs and their connections to these large-scale climate modes in an ocean model (ACCESS–OM2) simulation driven by a prescribed atmosphere from JRA55–do over 1959– 2018. Rather than analysing sea surface temperature, the WA MHWs and climate mode indices were characterized and investigated in vertically averaged temperature (VAT) to ~300m depth to afford the longer ocean dynamic time scales, including remote oceanic connections. We develop a cyclostationary linear inverse model (CS-LIM; from 35°S–10°N, across the Indo-Pacific Ocean), to investigate the relative contributions of La Niña VAT and positive IOD VAT to the predictability of WA VAT MHWs. Using a large ensemble of CS-LIM simulations, we found that ~50% of WA MHWs were preceded about 5 months by La Niña, and 30% of the MHWs by positive IOD about 20 months prior. While precursor La Niña or positive IOD, on their own, were found to correspond with increased WA MHW likelihood in the months following (~2.7 times or ~1.5 times more likely than by chance, respectively), in combination these climate mode phases were found to produce the largest enhancement in MHW likelihood (~3.2 times more likely than by chance). Additionally, we found that stronger and longer La Niña and/or positive IOD tend to lead stronger and longer WA MHWs.