Traditionally, vulnerability assessments for climate change risks rely on randomized precipitation possibilities and lack a physical science basis. Additionally, limitations in representing local precipitation fields in General Circulation Models (GCM) hinder the usefulness of precipitation projections of future hydroclimatic extremes. To address these challenges in climate risk management, this study investigates the ability of large-scale atmospheric circulation patterns, or weather regimes (WRs), to explain local precipitation and regional precipitation drivers. Utilizing a Non-Homogeneous Hidden Markov Chain approach, we identified six primary WRs for South Africa, each exhibiting distinct seasonality. Three WRs, associated with higher precipitation near Cape Town, dominate in winter, while two WRs linked to lower precipitation are prevalent in summer. The WR-precipitation relationship in South Africa appears to be influenced by topographic features (e.g., The Great Escarpment and Cape Fold Mountains) and ocean currents (Agulhas and Benguela), leading to distinct spatial precipitation responses to regional WR configurations. The seasonal frequency of WRs in the South African region has shifted dramatically in the past 20 years. Changes in WR composition, particularly the replacement since 2010 of a WR associated with rain in Cape Town with a dry WR, may help to explain the worsening drought conditions in the past decade. During the 2015-2017 Day Zero drought in Cape Town, the regional WR associated with dryness in Cape Town occurred more frequently than the historical average. The insights from the WR-precipitation analysis can be used to inform WR-based stochastic weather generators for vulnerability assessment and climate change adaptation planning.