A key finding from analysis of EWP is a trend towards wetter winters (DJF) and drier summers (JJA) (Gregory et al., 1991 ; Jones et al., 1997 ; Jones and Conway, 1997 ; Jenkins et al., 2008 ; Kendon et al., 2017 ). This is consistent with regional climate model projections for coming decades (Maisey et al., 2018 ). However, both trends depend critically on the early series, prior to the pioneering work of George J. Symons and Charles Higman Griffith who, in the 1860s, led the standardization of rain gauge design, exposure and measurement practice in the UK (Walker, 2010 ; Burt, 2013 ). Despite widespread application of EWP over more than three decades, the early record and associated trends have not been re‐evaluated in light of modern evidence, while homogeneity issues have been recognized at constituent gauges (Wigley et al., 1984 ; Burt and Howden, 2011 ). Here, we statistically reconstruct EWP using independent, quality‐assured temperature, pressure and circulation indices to examine the early record.

Long‐term, quality‐assured records are essential for understanding climate variability and change (Brázdil et al., 2005 ). Globally, few such records extend to the 18th century, particularly for precipitation. The England Wales Precipitation (EWP) series (Wigley et al., 1984 ; Wigley and Jones, 1987 ; Alexander and Jones, 2000 ; Simpson and Jones, 2014 ) is a rare exception, providing a continuous monthly record from 1766. This areal precipitation series, derived from five rainfall regions to avoid sampling bias, is regarded as homogenous (Wigley et al., 1984 ; Croxton et al., 2006 ) and is routinely updated by the UK Met Office (UKMO) Hadley Centre. Over more than three decades, EWP has found widespread use in understanding precipitation variability in Northwest Europe (e.g., Murphy et al., 2018 ), trend detection (Gregory et al., 1991 ; Jones et al., 1997 ; Jones and Conway, 1997 ; Jenkins et al., 2008 ; Kendon et al., 2017 ; 2018 ), evaluation of climate model simulations (Dessai and Hulme, 2008 ), as a proxy for mid‐latitude atmospheric circulation (Luterbacher et al., 2001 ), a predictor in long‐term European gridded precipitation data sets (Pauling et al., 2006 ; Casty et al., 2007 ), the assessment of droughts (Marsh et al., 2007 ) and extremes (Beran, 2002 ), tree‐ring reconstructions (Rinne et al., 2013 ) and as a benchmark for other regional series (Noone et al., 2016 ).

2 DATA AND METHODS

Monthly EWP data were accessed from the UKMO (https://www.metoffice.gov.uk/hadobs/hadukp/) and the winter (DJF), spring (MAM), summer (JJA) and autumn (SON) series compiled. Winters are specified by the year in which January falls. We reconstruct EWP seasonal precipitation using multiple linear regression. Emphasis is placed on the winter (henceforth EWP w ) and summer (henceforth EWP s ) series given the previously identified trends in these seasons.

Predictors are based on available long‐term observed and reconstructed sea level pressure (SLP), temperature and wind direction series (listed in Table 1 and described below). Predictor selection and estimation of model coefficients were undertaken for the calibration period 1900–2002; the period of optimum overlap between EWP and predictors. Simulations were evaluated for the years 1870–1899, before focusing attention on our reconstruction period 1767–1869 (i.e., before the work of Symons and Higman Griffith).

Table 1. Overview of predictors and associated data used for model building, together with their relation to EWP Predictor data sets and period used Relation to EWP CET 1767–2002 (Manley, 1974 et al., 1992 Warm winters tend to be wetter winters through enhanced advection. WI 1767–2002 (Barriopedro et al., 2014 et al., 2010 Measure of the persistence of westerly winds beneath the exit zone of the North Atlantic extratropical jet stream. PL 1767–2002 (Cornes et al., 2013 Measure of westerly air flow over Northwest Europe. Used as an indicator of the state of the NAOI. LSLP 1767–2002 (Cornes et al., 2012a MSLP for the city of London. High pressure is associated with lower precipitation totals. Leading EOF of reconstructed gridded SLP (KEOF) 1767–2002 (Küttel et al., 2010 Surrogate for the NAOI from independent SLP reconstructions Average of correlated grids from reconstructed gridded SLP (KAVG) 1767–2002 (Küttel et al., 2010 et al., 2018 SLP for area representing 50o–60oN, 10oW–5°E high pressure associated with lower precipitation totals.

2.1 Predictor variables Central England Temperature (CET) : (Manley, 1974 et al., 1992 et al., 2018 https://www.metoffice.gov.uk/hadobs/hadcet/) and seasonal mean temperature extracted.

: (Manley, https://www.metoffice.gov.uk/hadobs/hadcet/) and seasonal mean temperature extracted. Westerly Index (WI) : A monthly index of atmospheric circulation variability over the North Atlantic from 1685–2008 (Barriopedro et al., 2014 et al., 2005 et al., 2005 et al., 2010 et al., 2014

: A monthly index of atmospheric circulation variability over the North Atlantic from 1685–2008 (Barriopedro London Sea Level Pressure (LSLP) : A 315‐year (1692–2007) daily series of mean SLP (MSLP) for the city of London (Cornes et al., 2012a et al., 2012a https://crudata.uea.ac.uk/cru/data/parislondon/).

: A 315‐year (1692–2007) daily series of mean SLP (MSLP) for the city of London (Cornes https://crudata.uea.ac.uk/cru/data/parislondon/). Paris London Index (PL) : An indicator of the state of the North Atlantic Oscillation Index (NAOI) over the years 1692–2007, providing a consistent measure of westerly airflow over Northwest Europe (Cornes et al., 2013 et al ., 2012a 2012b https://crudata.uea.ac.uk/cru/data/parislondon/).

: An indicator of the state of the North Atlantic Oscillation Index (NAOI) over the years 1692–2007, providing a consistent measure of westerly airflow over Northwest Europe (Cornes ., https://crudata.uea.ac.uk/cru/data/parislondon/). Küttel EOF : Küttel et al . ( 2010 2006 ftp://ftp.ncdc.noaa.gov/pub/data/paleo/historical/kuettel2009slp.txt). We derived the first Empirical Orthogonal Function (EOF) of the gridded SLP (KEOF) as a measure of the NAOI.

: Küttel . ( ftp://ftp.ncdc.noaa.gov/pub/data/paleo/historical/kuettel2009slp.txt). We derived the first Empirical Orthogonal Function (EOF) of the gridded SLP (KEOF) as a measure of the NAOI. Küttel AVG : We use the Küttel et al . ( 2010 Küttel AVG as the standardized mean MSLP anomaly in a box centred over the UK (50°–60°N, 10°W–5°E) (Baker et al., 2018

: We use the Küttel . ( as the standardized mean MSLP anomaly in a box centred over the UK (50°–60°N, 10°W–5°E) (Baker Küttel Grids: We also identify individual 5 × 5° grids that provide significant predictors of EWP w and EWP s . These grids are numbered using the naming convention of the original data set (Küttel et al., 2010 Long‐term predictor variables that are independent from EWP and describe important dynamics of regional precipitation were available for model building. While these have been independently quality assured, we acknowledge that each may be subject to unknown inhomogeneities. Our predictors were:

2.2 Model selection, fitting and testing for seasonal EWP Multiple linear regression models were developed for each season with predictors selected during the calibration period 1900–2002 (the maximum overlap for available predictors as gridded SLP reconstructions end in 2002). Both the predictand (observed EWP) and potential predictors were checked for compliance with the assumptions of ordinary least squares regression. Winter and autumn CET were found to be non‐normally distributed, so were transformed using a Box‐Cox Transformation (estimated Lambda 1.4 and 1.2, respectively) (Osborne, 2010). Observed autumn EWP was also found to be non‐normal (p = .04), however, for ease of interpretation and plotting was not transformed. Models were selected for use provided they: (a) explain more than 70% of variance (R2) in winter and summer EWP and more than 60% in spring and autumn EWP over the full calibration period; (b) predictors are a statistically significant (p < .05) addition to the model and (c) are unaffected by multicollinearity (indicated by the Variance Inflation Factor). To explore the effect of uncertainty in calibration data on simulations, selected models were fitted using 1,000 bootstrap re‐samples of 50 years (with replacement) from the calibration data set. These re‐samples were then used to establish (a) a median simulation for each selected model; (b) an ensemble median simulation across all models and (c) 95% confidence intervals across all models. All resampled simulations were assessed to ensure the iid assumption of OLS residuals. Simulations were evaluated using independent data for the period 1870–1899 before examining reconstructions for the period 1767–1869. Decadal means were derived using a centred moving average.

2.3 Investigating winter model residuals Manley's London sleet and snow (SS) series was used to investigate the potential role of snow under‐catch on EWP w model residuals. The full SS series runs from 1668–1974. These data were originally tabulated by Gordon Manley and Elizabeth Shaw drawing on numerous gauges in the greater London area (Manley, 1969). The data are assumed to represent an elevation up to 200 ft (Manley, 1969). While plotted and discussed in Manley's original 1969 paper, the data were never digitized. Following his death in 1980, Manley's notes were deposited by Mrs. Audrey Manley (his widow) in Cambridge University Library. These are housed in a dedicated archive (Reference: GB 12 MS.Add.8386) with his notes on snow contained in Box 16/27. These notes remain in the original envelopes in which they were deposited. The lead author visited this archive in December 2017 and scanned the data relating to the London SS series. These were later transcribed using double keying to avoid data entry errors. Manley subjected the series to substantial quality control. The series is never based on fewer than three good quality stations back to 1811 and at least two stations between then and the 1680s (Manley, 1969). Correction was made for the adoption of the Gregorian calendar in September 1752. Observations of the occurrence of sleet and snow were categorized into four quality grades based on the assumed “alertness” of the observer: A – data from first‐class airfields and observatories; B – data from keen climatological observers; C – data from sites at which one daily visit was made to the instruments and D – data from stations overseen by observers who had other duties (Manley, 1969). Table S1 provides a list of the stations contributing to the London SS series, together with their altitude, observer surname and quality control grade assigned by Manley to the data. Comparisons of model residuals and the London SS series are undertaken using centred decadal means. Where correlations are presented, they are derived using Pearson's correlation.

2.4 Additional models developed Regression models are also derived for selected constituent EWP series. In order to aid the interpretation of EWP s model residuals, reconstructions of summer precipitation at Kew and Oxford were derived. Both stations are influential constituents of EWP for the south east England region and among the longest available series in the UK. Precipitation totals for both stations were obtained from CRU (https://crudata.uea.ac.uk/cru/data/UK_IR_rainfall_data/) and summer (JJA) totals extracted. The same predictors and study design as above were used for reconstructing both series, with the exception that only the best fit model was employed for each station.