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We present K2SC (K2 Systematics Correction), a Python pipeline to modelinstrumental systematics and astrophysical variability in light curves from theK2 mission. K2SC uses Gaussian process regression to model position-dependentsystematics and time-dependent variability simultaneously, enabling the user toremove both (e.g., for transit searches) or to remove systematics whilepreserving variability (for variability studies). For periodic variables, K2SCautomatically computes estimates of the period, amplitude and evolutiontimescale of the variability. We apply K2SC to publicly available K2 data fromcampaigns 3--5, showing that we obtain photometric precision approaching thatof the original Kepler mission. We compare our results to other publiclyavailable K2 pipelines, showing that we obtain similar or better results, onaverage. We use transit injection and recovery tests to evaluate the impact ofK2SC on planetary transit searches in K2 PDC (Pre-search Data Conditioning)data, for planet-to-star radius ratios down Rp/Rstar = 0.01 and periods up to P= 40 d, and show that K2SC significantly improves the ability to distinguishbetween correct and false detections, particularly for small planets. K2SC canbe run automatically on many light curves, or manually tailored for specificobjects such as pulsating stars or large amplitude eclipsing binaries. It canbe run on ASCII and FITS light curve files, regardless of their origin. Boththe code and the processed light curves are publicly available, and we provideinstructions for downloading and using them. The methodology used by K2SC willbe applicable to future transit search missions such as TESS and PLATO.

K2SC: Flexible systematics correction and detrending of K2 light curves using Gaussian Process regression