X is rank deficient to within machine precision. Linear regression of mean

Hello, so I am writing code to do a linear regression on 2 variables - each variable exists on 5 different nc files, so I have to draw the data from them first.

The code is below. It is generating sensible figures and statistics. However, I keep getting the amber warning:

"Warning: X is rank deficient to within machine precision"

>In regress (line 84)

>In mean_ctl2 (line 54)

Is this perhaps because the code is generating the mean slopes (rather than the slope of the mean variable)?

% Loop through the five files - 001 to 005, read and save what is needed
num_files = 5;
for i = 1 : num_files
    lat = ncread(sprintf('f.io192.00%d_atm_h1_yearmean_selname.nc', i), 'lat');
    lon = ncread(sprintf('f.io192.00%d_atm_h1_yearmean_selname.nc', i), 'lon');
    time = ncread(sprintf('f.io192.00%d_atm_h1_yearmean_selname.nc', i), 'time');
    TSA = ncread(sprintf('f.io192.00%d_lnd_h1_yearmean_selname.nc', i), 'TSA');
    TXx = ncread(sprintf('f.io192.00%d_atm_h1_yearmean_selname.nc', i), 'TXx');
    TXxs = cat(4, TXxs, TXx);
    TSAs = cat(4, TSAs, TSA);
end

% Create storage for regression
% Number of lat and lon and times must be the same across all 5 files
M = numel(lon);
N = numel(lat);
slope_TXx = zeros(M, N, num_files);
intercept_TXx = zeros(M, N, num_files);
slope_TSA = zeros(M, N, num_files);
intercept_TSA = zeros(M, N, num_files);
T = numel(time);

% Regress each lat/lon location
for i = 1 : M
    for j = 1 : N
        % Take each file
        for k = 1 : num_files
            % Get all time instances of each lat/lon location for TXx
            y = squeeze(TXxs(i, j, :, k));

            % Create regression problem and solve
            x = [ones(T, 1) time];
            %c = x \ y;
            c = regress(y, x);
            intercept_TXx(i, j, k) = c(1);
            slope_TXx(i, j, k) = c(2);
          
            % Repeat for TSA
            y = squeeze(TSAs(i, j, :, k));

            % Create regression problem and solve
            %x = [ones(T, 1) time];
            %c = x \ y;
            c = regress(y, x);
            intercept_TSA(i, j, k) = c(1);
            slope_TSA(i, j, k) = c(2);
    
        end
    end
end


% Find the mean of each file
mean_slope_TXx = mean(slope_TXx, 3);
mean_slope_TSA = mean(slope_TSA, 3);
mean_intercept_TXx = mean(intercept_TXx, 3);
mean_intercept_TSA = mean(intercept_TSA, 3);