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A multi-season convection permitting regional climate simulation of the Maritime Continent using the Met Office Unified Model with 2.2-km grid spacing is presented and evaluated. The simulations pioneer the use of atmosphere-ocean coupling with the multi-column K profile parametrisation (KPP) mixed layer ocean model in atmospheric convection permitting climate simulations. Comparisons are made against a convection parametrised simulation in which it is nested, and which in turn derives boundary conditions from ERA5 reanalysis. This paper describes the configuration, performance of the mean state and variability of the two simulations compared against observational datasets. The models both have minor sea surface temperature (SST) and wet precipitation biases. The diurnal cycle, representation of equatorial waves and relationship between SST and precipitation are all improved in the convection permitting model compared to the convection parametrised model. The MJO is present in both models with a faster than observed propagation speed. However, it is unclear whether fidelity of the MJO simulation is inherent to the model or whether it predominantly arises from the forcing at the boundaries. [ABSTRACT FROM AUTHOR]

This study aims to understand the process that determines the regional rainfall distribution in the equatorial South China Sea (SCS) and its surrounding during the cold surge (CS) days by looking at the different vorticity phases of active equatorial Rossby (R1) waves and the dynamics of the CSs related to the R1 wave. The CS can be thought of as an anticyclonic outflow from midlatitude and behaves like a dispersive group of R1 meridional modes. From the study, it shows the vorticity of the R1 wave arranges itself in such a way that the anticyclonic phase is located to the west and the cyclonic phase is located to the east of the cold CS axis. The CS winds in the equatorial SCS occur early, remain longer, and possess a larger zonal component in the active cyclonic phase compared with the active anticyclonic phase. During the active cyclonic vorticity phase, the rainfall anomalies pattern in the equatorial SCS changes from a "tick‐mark‐like" pattern to a "V‐shape" pattern as a consequence of the westward propagation of the wave. The probability of rainfall increases near the centre of cyclonic vorticity, but for anticyclonic vorticity the probability increases in the southern part of the vorticity centre. There is both increased (decreased) intensity and probability of extreme rainfall events during the active cyclonic (anticyclonic) vorticity phase on CS days. Overall, the rainfall anomalies are largely dominated by changes in the intensity on wet days rather than the number of wet days. These changes indicate the roles of the R1 wave vorticity phase in the rainfall distribution during CS days. The timing of enhancement and suppression of rain will provide additional valuable information to help improve forecast extreme weather events. [ABSTRACT FROM AUTHOR]

While skilful forecasts of heavy rainfall are highly desirable for weather warnings and mitigating impacts, forecasting such events is notoriously difficult, even with the most advanced numerical weather prediction models, due to the strong dependence on convective‐scale processes. The large‐scale circulation, on the other hand, is typically more predictable. Weather patterns (WPs) are a set of circulation types obtained statistically that can be used to characterize regional weather and harness the predictability of the large‐scale circulation. In this work we produce pattern‐conditioned probabilistic rainfall forecasts by projecting the horizontal winds from the Met Office GloSea5 prediction system on to WPs and then using the observed relationship between each WP and rainfall estimated by satellite. The WPs are derived following a novel two‐tier clustering technique: the WPs in the first tier represent planetary‐scale variability, such as El Niño–Southern Oscillation (ENSO), while the WPs in the second tier capture synoptic‐scale variability. We investigate WP predictability as well as the improvement in skill of subseasonal rainfall forecasts gained by this technique. GloSea5 predicts the WP occurrence with skill extending beyond lead day 10. The pattern‐conditioned rainfall forecasts were evaluated against climatological forecasts and model‐simulated rainfall hindcasts. We show that the pattern‐conditioned forecasts are skilful and outperform the model‐simulated rainfall hindcasts for lead times extending to days 10–20, depending on the specific exceedance criteria and region. Spatial aggregation leads to increased levels of skill, but not to a significant extension of the skilful prediction horizon. These results constitute a fundamental step for the development of subseasonal prediction systems for Southeast Asia. [ABSTRACT FROM AUTHOR]

Two sets of weather patterns describing variability in 850 hPa winds in Southeast Asia are presented and compared. Patterns are calculated using EOF/k-means clustering with and without imposing a separation between planetary-scale and regional-scale circulation features. The former are labelled as tiered patterns while the latter are referred to as flat. The ability of the patterns to distinguish between known modes of tropical circulation variability is examined. This includes climate modes such as the seasonal monsoons, the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) as well as sub-seasonal modes including cold surges, phases of the MJO and Boreal summer intraseasonal oscillation (BSISO), tropical cyclones, Borneo vortices and equatorial waves. All these modes are well captured by the weather patterns except for the equatorial waves and the IOD. The tiered patterns are shown to better describe large-scale modes of variability, while the flat patterns better describe the synoptic variability. Both sets of weather patterns are then used to study the likelihood of heavy precipitation depending on synoptic circulation by considering the regime-conditioned probability of high-percentile precipitation using the satellite-derived Global Precipitation Measurement (GPM) dataset. It is shown that the pattern centroids explain up to 10% of the seasonally anomalous precipitation over land, and that a perfect weather pattern forecast would outperform a perfect MJO forecast. These weather patterns show promising potential in extending the useful forecast range for the risk of heavy precipitation, dependent on their forecastability. [ABSTRACT FROM AUTHOR]