REVIEW PAPER Variability and Predictability of Summer Monsoon Rainfall over Pakistan Muhammad Adnan 1 & Firdos Khan 2 & Nadia Rehman 1 & Shaukat Ali 1 & Sher Shah Hassan 1 & Muhammad Mubashar Dogar 1 & Shahbaz Mehmood 1 & Shabehul Hasson 3,4 Received: 2 July 2019 /Revised: 19 December 2019 /Accepted: 19 January 2020 # Korean Meteorological Society and Springer Nature B.V. 2020 Abstract Rainfall variability associated with the South Asian Summer Monsoon has increased in recent decades, particularly at the northwestern monsoon margins over Pakistan, leading to more frequent and intense hydro-meteorological extremes that have adversely affected the agrarian economy, water and food security in the country. Devising effective strategies to ensure sustain- able development in Pakistan thus requires that the monsoonal rainfall be predicted on an inter-annual scale. Here, we predicted the inter- and intra-annual variability of the monsoonal rainfall over Pakistan and its possible drivers using a linear statistical forecast model of the principal component (PC) regression analysis. For this purpose, highly correlated PCs of the National Centre for Environmental Prediction (NCEP) based sea level pressure, horizontal and meridional winds to the observed rainfall for the period 2001–2013 were ingested in a stepwise multiple regression model, which was further validated for the duration of 2014–2015. Our results suggest that featuring correlation coefficient, mean absolute error, mean bias, and root mean square error of 0.75, 42.23, -14.92 and 60.65, respectively, the model exhibits robust skill in predicting the inter-annual monsoonal rainfall variability at its extreme northwestern margins over Pakistan. Keywords Rainfall . Inter-annual variability . Principal component . Monsoon . Predictability . Downscaling . Pakistan 1 Introduction Precipitation is a key component of the hydrological cycle and one of the most important parameters for a range of natural and socio-economic systems: water resources management, agriculture and forestry, tourism and food production (Schmidli et al. 2007). Pakistan is an agrarian economy where the agriculture-sector contributes around 22.88% to the Gross Domestic Product (Young et al. 2019). Such significant agri- cultural contribution depends heavily on the seasonal water supplies, predominantly from the South Asian Summer Monsoonal (SASM) rainfall (Rehman et al. 1997). Asian sum- mer monsoon (ASM) rainfall contributes over 60%, and it is the major water resource supporter of the world population and ecosystem, however seasonal predictability and variabil- ity remains a long-standing challenge (Kang et al. 2004; Lee et al. 2011a, 2011b; Wang et al. 2009). However, the behavior of the SASM rainfall is particularly erratic at its extreme north- western margins over Pakistan. Huge inter-annual variations are observed in rainfall amounts, which had far-reaching im- pacts on the economy of the country. (Ali et al. 2019) has also analyzed spatio-temporal variability of summer monsoon on- set over Pakistan and showed that the temporal analysis of monsoon onset has been shifting from first week of July to last week of June. This variability can have major impacts on agriculture crops. Since long time, India Meteorological Department (IMD) noted abrupt temporal and spatial evolu- tion in various meteorological parameters like winds, precip- itation and Outgoing Longwave Radiation (OLR). This dec- laration process suffered from subjectivity as no specific Responsible Editor: Edvin Aldrian. * Muhammad Adnan scarpion982@gmail.com 1 Global Change Impact Studies Centre (GCISC), Ministry of Climate Change, Islamabad, Pakistan 2 School of Natural Sciences (SNS), National University of Sciences and Technology (NUST), Islamabad, Pakistan 3 Institute of Geography, Centre for Earth System and Sustainability (CEN), University of Hamburg, Hamburg, Germany 4 Department of Space Science, Institute of Space Technology, Islamabad, Pakistan Asia-Pacific Journal of Atmospheric Sciences https://doi.org/10.1007/s13143-020-00178-2 Online ISSN 1976-7951 Print ISSN 1976-7633 Korean Meteorological Society