The Asian summer monsoon [ASM) is one of the largest seasonal atmospheric phenomena involving huge moisture transports from ocean to land. This moisture transport ultimately makes the monsoon special as the main source of precipitation affecting livelihoods and infrastructure in the most populated region on Earth. Despite interannual variability of the ASM being a relatively small fraction of seasonal mean rainfall (e.g., for India, the coefficient of variation is around 10%), society is so finely tuned to the monsoon that variations on annual to intraseasonal time scales can cause huge problems relating to flood (infrastructure damage; health) and drought (crop damage; public water supply; hydro-electric generation). The shorter-time scale monsoon intraseasonal variations (MISV hereafter) show the strongest variability, with active and break conditions leading to more damaging impacts. While slow variations of the atmospheric lower boundary forcing such as snow cover and the El Niño-Southern Oscillation (ENSO) lend predictability to seasonal rainfall anomalies in the tropics [Charney and Shukla, 1981], this predictability is limited by MISV [Brankovic and Palmer, 2000]. Recent assessments of state-of-the-art general circulation models (GCMs] show that few show skill at simulating all important characteristics of MISV [Sperber and Annamalai, 2008] highlighting the importance of better understanding the dynamics and predictability of the ASM. Furthermore, the relationship between the intraseasonal component of monsoon variability, the seasonal mean, and large-scale forcing conditions is unclear. Among the hypotheses put forward is that of a Lorenz model [Palmer, 1994] with chaotic fluctuations between active and break monsoon phases. While some studies have suggested that total seasonal rainfall may be broken down into a seasonal mean component forced by lower boundary conditions and the statistics of inherently unpredictable MISV [Krishnamurthy and Shukla, 2000, 2007], others suggest that MISV itself may be somehow related to boundary conditions and thus predictable: the large-scale forcing predisposing the system in a chaotic model to reside in one regime more than the other [Webster et al., 1998]. In fact, in relation to rainfall in central India, Palmer  suggested that, under a given forcing, one of the predominant locations for convection (central India and the equatorial Indian Ocean Tropical Convergence Zone region) will be favored over the other according to the large-scale forcing.
 The ASM is a highly nonlinear and high dimensional phenomenon; one way to understand ASM dynamics is to find ways to reduce the dimensionality of the system in a way that could help capture the main features of its nonlinear behavior. Sperber et al.  (SP00 hereafter) used empirical orthogonal function (EOF) analysis to reduce the dimensionality of National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis winds, identifying a common mode of variability on intraseasonal and interannual time scales. SP00 showed the interesting result that a probability density function (PDF) of an intraseasonal principal component time series could be translated towards negative or positive values according to seasonal mean conditions. However, only a small subset of MISV can be perturbed in this way by large scale forcing, and SP00 found no bimodality, the PDF being Gaussian, suggesting that the dominant modes of MISV are due to (inherently stochastic) internal processes, hence placing a limit on their predictability as part of the monsoon. Instead, Straus and Krishnamurthy  showed that bimodality only exists under certain conditions. Clear bimodality of the South and East Asian summer monsoon activity, however, has not been established with certainty. In an earlier study by the authors [Turner and Hannachi, 2010, TH10 hereafter], one-dimensional Gaussian mixture model analysis was performed on the leading mode of an intraseasonal outgoing longwave radiation (OLR) index of ASM convection. The OLR was unimodal but skewed, and the skewness was interpreted using a mixture model in terms of two intraseasonal monsoon regimes, namely active and break phases. TH10 suggested a preference for break conditions over India during seasonally weak monsoons. Note that this is not simply a trivial point, since the weak monsoon season may be caused by a country-wide seasonal anomaly related to large-scale forcing such as ENSO [Krishnamurthy and Shukla, 2000, 2007], even in the absence of any active/break activity.
 In this paper, we advance on earlier studies by using the isometric feature mapping [Isomap) method [Tenenbaum et al., 2000] on sea-level pressure (SLP) to reduce the dimensionality of the ASM while maintaining nonlinear components. We then apply a multivariate Gaussian mixture model to estimate the PDF of the ASM within the obtained Isomap low-dimensional space. Isomap is based on interpoint distances rather than explained variance (as in EOFs) and is therefore more suited to study the nonlinear structure of the ASM. The data and methodology are described in section 2. Section 3 discusses the results and the implications are presented in the last section.